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Li M, Li S, Han Y, Zhang T. GVC-Net:Global Vascular Context Network for Cerebrovascular Segmentation Using Sparse Labels. Ing Rech Biomed 2022. [DOI: 10.1016/j.irbm.2022.05.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Huang S, Han X, Fan J, Chen J, Du L, Gao W, Liu B, Chen Y, Liu X, Wang Y, Ai D, Ma G, Yang J. Anterior Mediastinal Lesion Segmentation Based on Two-Stage 3D ResUNet With Attention Gates and Lung Segmentation. Front Oncol 2021; 10:618357. [PMID: 33634027 PMCID: PMC7901488 DOI: 10.3389/fonc.2020.618357] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Accepted: 12/15/2020] [Indexed: 01/13/2023] Open
Abstract
OBJECTIVES Anterior mediastinal disease is a common disease in the chest. Computed tomography (CT), as an important imaging technology, is widely used in the diagnosis of mediastinal diseases. Doctors find it difficult to distinguish lesions in CT images because of image artifact, intensity inhomogeneity, and their similarity with other tissues. Direct segmentation of lesions can provide doctors a method to better subtract the features of the lesions, thereby improving the accuracy of diagnosis. METHOD As the trend of image processing technology, deep learning is more accurate in image segmentation than traditional methods. We employ a two-stage 3D ResUNet network combined with lung segmentation to segment CT images. Given that the mediastinum is between the two lungs, the original image is clipped through the lung mask to remove some noises that may affect the segmentation of the lesion. To capture the feature of the lesions, we design a two-stage network structure. In the first stage, the features of the lesion are learned from the low-resolution downsampled image, and the segmentation results under a rough scale are obtained. The results are concatenated with the original image and encoded into the second stage to capture more accurate segmentation information from the image. In addition, attention gates are introduced in the upsampling of the network, and these gates can focus on the lesion and play a role in filtering the features. The proposed method has achieved good results in the segmentation of the anterior mediastinal. RESULTS The proposed method was verified on 230 patients, and the anterior mediastinal lesions were well segmented. The average Dice coefficient reached 87.73%. Compared with the model without lung segmentation, the model with lung segmentation greatly improved the accuracy of lesion segmentation by approximately 9%. The addition of attention gates slightly improved the segmentation accuracy. CONCLUSION The proposed automatic segmentation method has achieved good results in clinical data. In clinical application, automatic segmentation of lesions can assist doctors in the diagnosis of diseases and may facilitate the automated diagnosis of illnesses in the future.
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Affiliation(s)
- Su Huang
- Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing, China
| | - Xiaowei Han
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Jingfan Fan
- Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing, China
| | - Jing Chen
- Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing, China
| | - Lei Du
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Wenwen Gao
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Bing Liu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Yue Chen
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Xiuxiu Liu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Yige Wang
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Danni Ai
- Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing, China
| | - Guolin Ma
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Jian Yang
- Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing, China
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Saroj SK, Kumar R, Singh NP. Fréchet PDF based Matched Filter Approach for Retinal Blood Vessels Segmentation. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 194:105490. [PMID: 32504830 DOI: 10.1016/j.cmpb.2020.105490] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/02/2019] [Revised: 03/20/2020] [Accepted: 03/31/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND AND OBJECTIVE Retinal pathology diseases such as glaucoma, obesity, diabetes, hypertension etc. have deadliest impact on life of human being today. Retinal blood vessels consist of various significant information which are helpful in detection and treatment of these diseases. Therefore, it is essential to segment these retinal vessels. Various matched filter approaches for segmentation of retinal blood vessels are reported in the literature but their kernel templates are not appropriate to vessel profile resulting poor performance. To overcome this, a novel matched filter approach based on Fréchet probability distribution function has been proposed. METHODS Image processing operations which we have used in the proposed approach are basically divided into three major stages viz; pre processing, Fréchet matched filter and post processing. In pre processing, principle component analysis (PCA) method is used to convert color image into grayscale image thereafter contrast limited adaptive histogram equalization (CLAHE) is applied on obtained grayscale to get enhanced grayscale image. In Fréchet matched filter, exhaustive experimental tests are conducted to choose optimal values for both Fréchet function parameters and matched filter parameters to design new matched filter. In post processing, entropy based optimal thresholding technique is applied on obtained MFR image to get binary image followed by length filtering and masking methods are applied to generate to a clear and whole vascular tree. RESULTS For evaluation of the proposed approach, quantitative performance metrics such as average specificity, average sensitivity and average accuracy and root mean square deviation (RMSD) are computed in the literature. We found the average specificity 97.24%, average sensitivity 72.78%, average accuracy 95.09% for STARE dataset while average specificity 97.61%, average sensitivity 73.07%, average accuracy 95.44% for DRIVE dataset. Average RMSD values are found 0.07 and 0.04 for STARE and DRIVE databases respectively. CONCLUSIONS From experimental results, it can be observed that our proposed approach outperforms over latest and prominent works reported in the literature. The cause of improved performance is due to better matching between vessel profile and Fréchet template.
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Affiliation(s)
- Sushil Kumar Saroj
- Department of Computer Science and Engineering, MMM University of Technology, Gorakhpur, India.
| | - Rakesh Kumar
- Department of Computer Science and Engineering, MMM University of Technology, Gorakhpur, India.
| | - Nagendra Pratap Singh
- Department of Computer Science and Engineering, National Institute of Technology, Hamirpur, India.
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Lo Vercio L, Del Fresno M, Larrabide I. Lumen-intima and media-adventitia segmentation in IVUS images using supervised classifications of arterial layers and morphological structures. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2019; 177:113-121. [PMID: 31319939 DOI: 10.1016/j.cmpb.2019.05.021] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Revised: 04/26/2019] [Accepted: 05/20/2019] [Indexed: 06/10/2023]
Abstract
BACKGROUND Intravascular ultrasound (IVUS) provides axial grey-scale images of blood vessels. The large number of images require automatic analysis, specifically to identify the lumen and outer vessel wall. However, the high amount of noise, the presence of artifacts and anatomical structures, such as bifurcations, calcifications and fibrotic plaques, usually hinder the proper automatic segmentation of the vessel wall. METHODS Lumen, media, adventitia and surrounding tissues are automatically detected using Support Vector Machines (SVMs). The classification performance of the SVMs vary according to the kind of structure present within each region of the image. Random Forest (RF) is used to detect different morphological structures and to modify the initial layer classification depending on the detected structure. The resulting classification maps are fed into a segmentation method based on deformable contours to detect lumen-intima (LI) and media-adventitia (MA) interfaces. RESULTS The modifications in the layer classifications according to the presence of structures proved to be effective improving LI and MA segmentations. The proposed method reaches a Jaccard Measure (JM) of 0.88 ± 0.08 for LI segmentation, compared with 0.88 ± 0.05 of a semiautomatic method. When looking at MA, our method reaches a JM of 0.84 ± 0.09, and outperforms previous automatic methods in terms of HD, with 0.51mm ± 0.30. CONCLUSIONS A simple modification to the arterial layer classification produces results that match and improve state-of-the-art fully-automatic segmentation methods for LI and MA in 20MHz IVUS images. For LI segmentation, the proposed automatic method performs accurately as semi-automatic methods. For MA segmentation, our method matched the quality of state-of-the-art automatic methods described in the literature. Furthermore, our implementation is modular and open-source, allowing for future extensions and improvements.
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Affiliation(s)
- Lucas Lo Vercio
- Pladema Institute, UNCPBA, Gral. Pinto 399, Tandil, Argentina; Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Argentina.
| | - Mariana Del Fresno
- Pladema Institute, UNCPBA, Gral. Pinto 399, Tandil, Argentina; Comisión de Investigaciones Científicas de la Provincia deBuenos Aires (CICPBA), Argentina
| | - Ignacio Larrabide
- Pladema Institute, UNCPBA, Gral. Pinto 399, Tandil, Argentina; Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Argentina
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Vigneshwaran V, Sands GB, LeGrice IJ, Smaill BH, Smith NP. Reconstruction of coronary circulation networks: A review of methods. Microcirculation 2019; 26:e12542. [DOI: 10.1111/micc.12542] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2018] [Revised: 01/25/2019] [Accepted: 02/27/2019] [Indexed: 12/12/2022]
Affiliation(s)
- Vibujithan Vigneshwaran
- Auckland Bioengineering Institute University of Auckland Auckland New Zealand
- Faculty of Engineering University of Auckland Auckland New Zealand
| | - Gregory B. Sands
- Auckland Bioengineering Institute University of Auckland Auckland New Zealand
| | - Ian J. LeGrice
- Department of Physiology University of Auckland Auckland New Zealand
| | - Bruce H. Smaill
- Auckland Bioengineering Institute University of Auckland Auckland New Zealand
| | - Nicolas P. Smith
- Auckland Bioengineering Institute University of Auckland Auckland New Zealand
- Faculty of Engineering University of Auckland Auckland New Zealand
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Machado S, Mercier V, Chiaruttini N. LimeSeg: a coarse-grained lipid membrane simulation for 3D image segmentation. BMC Bioinformatics 2019; 20:2. [PMID: 30606118 PMCID: PMC6318983 DOI: 10.1186/s12859-018-2471-0] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2018] [Accepted: 11/06/2018] [Indexed: 11/15/2022] Open
Abstract
Background 3D segmentation is often a prerequisite for 3D object display and quantitative measurements. Yet existing voxel-based methods do not directly give information on the object surface or topology. As for spatially continuous approaches such as level-set, active contours and meshes, although providing surfaces and concise shape description, they are generally not suitable for multiple object segmentation and/or for objects with an irregular shape, which can hamper their adoption by bioimage analysts. Results We developed LimeSeg, a computationally efficient and spatially continuous 3D segmentation method. LimeSeg is easy-to-use and can process many and/or highly convoluted objects. Based on the concept of SURFace ELements (“Surfels”), LimeSeg resembles a highly coarse-grained simulation of a lipid membrane in which a set of particles, analogous to lipid molecules, are attracted to local image maxima. The particles are self-generating and self-destructing thus providing the ability for the membrane to evolve towards the contour of the objects of interest. The capabilities of LimeSeg: simultaneous segmentation of numerous non overlapping objects, segmentation of highly convoluted objects and robustness for big datasets are demonstrated on experimental use cases (epithelial cells, brain MRI and FIB-SEM dataset of cellular membrane system respectively). Conclusion In conclusion, we implemented a new and efficient 3D surface reconstruction plugin adapted for various sources of images, which is deployed in the user-friendly and well-known ImageJ environment.
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Affiliation(s)
- Sarah Machado
- Marcos González Gaitán lab, University of Geneva, Department of Biochemistry, quai Ernest-Ansermet 30, Geneva, 1211, Switzerland
| | - Vincent Mercier
- Aurélien Roux lab, University of Geneva, Department of Biochemistry, quai Ernest-Ansermet 30, Geneva, 1211, Switzerland
| | - Nicolas Chiaruttini
- Aurélien Roux lab, University of Geneva, Department of Biochemistry, quai Ernest-Ansermet 30, Geneva, 1211, Switzerland.
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Chung M, Lee J, Chung JW, Shin YG. Accurate liver vessel segmentation via active contour model with dense vessel candidates. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 166:61-75. [PMID: 30415719 DOI: 10.1016/j.cmpb.2018.10.010] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2018] [Revised: 09/03/2018] [Accepted: 10/01/2018] [Indexed: 06/09/2023]
Abstract
BACKGROUND AND OBJECTIVE The purpose of this paper is to propose a fully automated liver vessel segmentation algorithm including portal vein and hepatic vein on contrast enhanced CTA images. METHODS First, points of a vessel candidate region are extracted from 3-dimensional (3D) CTA image. To generate accurate points, we reduce 3D segmentation problem to 2D problem by generating multiple maximum intensity (MI) images. After the segmentation of MI images, we back-project pixels to the original 3D domain. We call these voxels as vessel candidates (VCs). A large set of MI images can produce very dense and accurate VCs. Finally, for the accurate segmentation of a vessel region, we propose a newly designed active contour model (ACM) that uses the original image, vessel probability map from dense VCs, and the good prior of an initial contour. RESULTS We used 55 abdominal CTAs for a parameter study and a quantitative evaluation. We evaluated the performance of the proposed method comparing with other state-of-the-art ACMs for vascular images applied directly to the original data. The result showed that our method successfully segmented vascular structure 25%-122% more accurately than other methods without any extra false positive detection. CONCLUSION Our model can generate a smooth and accurate boundary of the vessel object and easily extract thin and weak peripheral branch vessels. The proposed approach can automatically segment a liver vessel without any manual interaction. The detailed result can aid further anatomical studies.
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Affiliation(s)
- Minyoung Chung
- School of Computer Science and Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 151-742, Korea
| | - Jeongjin Lee
- School of Computer Science and Engineering, Soongsil University, 369 Sangdo-Ro, Dongjak-Gu, Seoul 156-743, Korea.
| | - Jin Wook Chung
- Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul 110-799, Korea
| | - Yeong-Gil Shin
- School of Computer Science and Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 151-742, Korea
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Khan KB, Khaliq AA, Jalil A, Iftikhar MA, Ullah N, Aziz MW, Ullah K, Shahid M. A review of retinal blood vessels extraction techniques: challenges, taxonomy, and future trends. Pattern Anal Appl 2018. [DOI: 10.1007/s10044-018-0754-8] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
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10
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An Overview of Segmentation Algorithms for the Analysis of Anomalies on Medical Images. JOURNAL OF INTELLIGENT SYSTEMS 2018. [DOI: 10.1515/jisys-2017-0629] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Abstract
Human disease identification from the scanned body parts helps medical practitioners make the right decision in lesser time. Image segmentation plays a vital role in automated diagnosis for the delineation of anatomical organs and anomalies. There are many variants of segmentation algorithms used by current researchers, whereas there is no universal algorithm for all medical images. This paper classifies some of the widely used medical image segmentation algorithms based on their evolution, and the features of each generation are also discussed. The comparative analysis of segmentation algorithms is done based on characteristics like spatial consideration, region continuity, computation complexity, selection of parameters, noise immunity, accuracy, and computation time. Finally, in this work, some of the typical segmentation algorithms are implemented on real-time datasets using Matlab 2010 software, and the outcome of this work will be an aid for the researchers in medical image processing.
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Arrieta C, Uribe S, Sing-Long C, Hurtado D, Andia M, Irarrazaval P, Tejos C. Simultaneous left and right ventricle segmentation using topology preserving level sets. Biomed Signal Process Control 2017. [DOI: 10.1016/j.bspc.2016.11.002] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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12
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Topology Adaptive Water Boundary Extraction Based on a Modified Balloon Snake: Using GF-1 Satellite Images as an Example. REMOTE SENSING 2017. [DOI: 10.3390/rs9020140] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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Kromer R, Shafin R, Boelefahr S, Klemm M. An Automated Approach for Localizing Retinal Blood Vessels in Confocal Scanning Laser Ophthalmoscopy Fundus Images. J Med Biol Eng 2016; 36:485-494. [PMID: 27688743 PMCID: PMC5020115 DOI: 10.1007/s40846-016-0152-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2015] [Accepted: 02/03/2016] [Indexed: 11/24/2022]
Abstract
In this work, we present a rules-based method for localizing retinal blood vessels in confocal scanning laser ophthalmoscopy (cSLO) images and evaluate its feasibility. A total of 31 healthy participants (17 female; mean age: 64.0 ± 8.2 years) were studied using manual and automatic segmentation. High-resolution peripapillary scan acquisition cSLO images were acquired. The automated segmentation method consisted of image pre-processing for gray-level homogenization and blood vessel enhancement (morphological opening operation, Gaussian filter, morphological Top-Hat transformation), binary thresholding (entropy-based thresholding operation), and removal of falsely detected isolated vessel pixels. The proposed algorithm was first tested on the publically available dataset DRIVE, which contains color fundus photographs, and compared to performance results from the literature. Good results were obtained. Monochromatic cSLO images segmented using the proposed method were compared to those manually segmented by two independent observers. For the algorithm, a sensitivity of 0.7542, specificity of 0.8607, and accuracy of 0.8508 were obtained. For the two independent observers, a sensitivity of 0.6579, specificity of 0.9699, and accuracy of 0.9401 were obtained. The results demonstrate that it is possible to localize vessels in monochromatic cSLO images of the retina using a rules-based approach. The performance results are inferior to those obtained using fundus photography, which could be due to the nature of the technology.
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Affiliation(s)
- Robert Kromer
- Department of Ophthalmology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246 Hamburg, Germany
| | - Rahman Shafin
- Department of Computer Science, University of Manitoba, Winnipeg, Canada
| | - Sebastian Boelefahr
- Department of Ophthalmology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246 Hamburg, Germany
| | - Maren Klemm
- Department of Ophthalmology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246 Hamburg, Germany
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Rough-probabilistic clustering and hidden Markov random field model for segmentation of HEp-2 cell and brain MR images. Appl Soft Comput 2016. [DOI: 10.1016/j.asoc.2016.03.010] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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Singh NP, Srivastava R. Retinal blood vessels segmentation by using Gumbel probability distribution function based matched filter. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2016; 129:40-50. [PMID: 27084319 DOI: 10.1016/j.cmpb.2016.03.001] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2015] [Revised: 02/26/2016] [Accepted: 03/01/2016] [Indexed: 05/16/2023]
Abstract
BACKGROUND AND OBJECTIVE Retinal blood vessel segmentation is a prominent task for the diagnosis of various retinal pathology such as hypertension, diabetes, glaucoma, etc. In this paper, a novel matched filter approach with the Gumbel probability distribution function as its kernel is introduced to improve the performance of retinal blood vessel segmentation. METHODS Before applying the proposed matched filter, the input retinal images are pre-processed. During pre-processing stage principal component analysis (PCA) based gray scale conversion followed by contrast limited adaptive histogram equalization (CLAHE) are applied for better enhancement of retinal image. After that an exhaustive experiments have been conducted for selecting the appropriate value of parameters to design a new matched filter. The post-processing steps after applying the proposed matched filter include the entropy based optimal thresholding and length filtering to obtain the segmented image. RESULTS For evaluating the performance of proposed approach, the quantitative performance measures, an average accuracy, average true positive rate (ATPR), and average false positive rate (AFPR) are calculated. The respective values of the quantitative performance measures are 0.9522, 0.7594, 0.0292 for DRIVE data set and 0.9270, 0.7939, 0.0624 for STARE data set. To justify the effectiveness of proposed approach, receiver operating characteristic (ROC) curve is plotted and the average area under the curve (AUC) is calculated. The average AUC for DRIVE and STARE data sets are 0.9287 and 0.9140 respectively. CONCLUSIONS The obtained experimental results confirm that the proposed approach performance better with respect to other prominent Gaussian distribution function and Cauchy PDF based matched filter approaches.
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Affiliation(s)
- Nagendra Pratap Singh
- Department of CSE, Indian Institute of Technology (BHU), Varanasi, UP 221005, India.
| | - Rajeev Srivastava
- Department of CSE, Indian Institute of Technology (BHU), Varanasi, UP 221005, India.
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Kirimasthong K, Rodtook A, Chaumrattanakul U, Makhanov SS. Phase portrait analysis for automatic initialization of multiple snakes for segmentation of the ultrasound images of breast cancer. Pattern Anal Appl 2016. [DOI: 10.1007/s10044-016-0556-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Kovács G, Hajdu A. A self-calibrating approach for the segmentation of retinal vessels by template matching and contour reconstruction. Med Image Anal 2016; 29:24-46. [DOI: 10.1016/j.media.2015.12.003] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2014] [Revised: 12/01/2015] [Accepted: 12/03/2015] [Indexed: 01/17/2023]
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Zhao F, Xie X. Energy minimization in medical image analysis: Methodologies and applications. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2016; 32:e02733. [PMID: 26186171 DOI: 10.1002/cnm.2733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2015] [Revised: 06/23/2015] [Accepted: 06/23/2015] [Indexed: 06/04/2023]
Abstract
Energy minimization is of particular interest in medical image analysis. In the past two decades, a variety of optimization schemes have been developed. In this paper, we present a comprehensive survey of the state-of-the-art optimization approaches. These algorithms are mainly classified into two categories: continuous method and discrete method. The former includes Newton-Raphson method, gradient descent method, conjugate gradient method, proximal gradient method, coordinate descent method, and genetic algorithm-based method, while the latter covers graph cuts method, belief propagation method, tree-reweighted message passing method, linear programming method, maximum margin learning method, simulated annealing method, and iterated conditional modes method. We also discuss the minimal surface method, primal-dual method, and the multi-objective optimization method. In addition, we review several comparative studies that evaluate the performance of different minimization techniques in terms of accuracy, efficiency, or complexity. These optimization techniques are widely used in many medical applications, for example, image segmentation, registration, reconstruction, motion tracking, and compressed sensing. We thus give an overview on those applications as well.
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Affiliation(s)
- Feng Zhao
- Department of Computer Science, Swansea University, Swansea, SA2 8PP, UK
| | - Xianghua Xie
- Department of Computer Science, Swansea University, Swansea, SA2 8PP, UK
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Lee O, Lee SH, Jeong SH, Kim J, Ryu HJ, Oh C, Son SW. A quantitative study of nanoparticle skin penetration with interactive segmentation. Med Biol Eng Comput 2015; 54:1469-79. [PMID: 26589318 DOI: 10.1007/s11517-015-1405-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2015] [Accepted: 10/03/2015] [Indexed: 01/30/2023]
Abstract
In the last decade, the application of nanotechnology techniques has expanded within diverse areas such as pharmacology, medicine, and optical science. Despite such wide-ranging possibilities for implementation into practice, the mechanisms behind nanoparticle skin absorption remain unknown. Moreover, the main mode of investigation has been qualitative analysis. Using interactive segmentation, this study suggests a method of objectively and quantitatively analyzing the mechanisms underlying the skin absorption of nanoparticles. Silica nanoparticles (SNPs) were assessed using transmission electron microscopy and applied to the human skin equivalent model. Captured fluorescence images of this model were used to evaluate degrees of skin penetration. These images underwent interactive segmentation and image processing in addition to statistical quantitative analyses of calculated image parameters including the mean, integrated density, skewness, kurtosis, and area fraction. In images from both groups, the distribution area and intensity of fluorescent silica gradually increased in proportion to time. Since statistical significance was achieved after 2 days in the negative charge group and after 4 days in the positive charge group, there is a periodic difference. Furthermore, the quantity of silica per unit area showed a dramatic change after 6 days in the negative charge group. Although this quantitative result is identical to results obtained by qualitative assessment, it is meaningful in that it was proven by statistical analysis with quantitation by using image processing. The present study suggests that the surface charge of SNPs could play an important role in the percutaneous absorption of NPs. These findings can help achieve a better understanding of the percutaneous transport of NPs. In addition, these results provide important guidance for the design of NPs for biomedical applications.
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Affiliation(s)
- Onseok Lee
- Department of Medical IT Engineering, College of Medical Sciences, Soonchunhyang University, Asan City, Chungnam, Korea
| | - See Hyun Lee
- Laboratory of Cell Signaling and Nanomedicine, Department of Dermatology, Korea University College of Medicine, Seoul, Korea
- Division of the Brain Korea 21 Project for Biomedical Science, Korea University College of Medicine, Seoul, Korea
| | - Sang Hoon Jeong
- Laboratory of Cell Signaling and Nanomedicine, Department of Dermatology, Korea University College of Medicine, Seoul, Korea
- Division of the Brain Korea 21 Project for Biomedical Science, Korea University College of Medicine, Seoul, Korea
| | - Jaeyoung Kim
- Research Institute for Skin Image, Korea University College of Medicine, Seoul, Korea
| | - Hwa Jung Ryu
- Laboratory of Cell Signaling and Nanomedicine, Department of Dermatology, Korea University College of Medicine, Seoul, Korea
- Division of the Brain Korea 21 Project for Biomedical Science, Korea University College of Medicine, Seoul, Korea
| | - Chilhwan Oh
- Research Institute for Skin Image, Korea University College of Medicine, Seoul, Korea
| | - Sang Wook Son
- Laboratory of Cell Signaling and Nanomedicine, Department of Dermatology, Korea University College of Medicine, Seoul, Korea.
- Division of the Brain Korea 21 Project for Biomedical Science, Korea University College of Medicine, Seoul, Korea.
- Research Institute for Skin Image, Korea University College of Medicine, Seoul, Korea.
- Department of Dermatology, Korea University Anam Hospital, Korea University College of Medicine, 126-1, Anam-dong 5-ga, Seongbuk-gu, Seoul, 136-705, Korea.
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Namías R, D'Amato JP, Del Fresno M, Vénere M, Pirró N, Bellemare ME. Multi-object segmentation framework using deformable models for medical imaging analysis. Med Biol Eng Comput 2015; 54:1181-92. [PMID: 26392182 DOI: 10.1007/s11517-015-1387-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2014] [Accepted: 09/01/2015] [Indexed: 11/30/2022]
Abstract
Segmenting structures of interest in medical images is an important step in different tasks such as visualization, quantitative analysis, simulation, and image-guided surgery, among several other clinical applications. Numerous segmentation methods have been developed in the past three decades for extraction of anatomical or functional structures on medical imaging. Deformable models, which include the active contour models or snakes, are among the most popular methods for image segmentation combining several desirable features such as inherent connectivity and smoothness. Even though different approaches have been proposed and significant work has been dedicated to the improvement of such algorithms, there are still challenging research directions as the simultaneous extraction of multiple objects and the integration of individual techniques. This paper presents a novel open-source framework called deformable model array (DMA) for the segmentation of multiple and complex structures of interest in different imaging modalities. While most active contour algorithms can extract one region at a time, DMA allows integrating several deformable models to deal with multiple segmentation scenarios. Moreover, it is possible to consider any existing explicit deformable model formulation and even to incorporate new active contour methods, allowing to select a suitable combination in different conditions. The framework also introduces a control module that coordinates the cooperative evolution of the snakes and is able to solve interaction issues toward the segmentation goal. Thus, DMA can implement complex object and multi-object segmentations in both 2D and 3D using the contextual information derived from the model interaction. These are important features for several medical image analysis tasks in which different but related objects need to be simultaneously extracted. Experimental results on both computed tomography and magnetic resonance imaging show that the proposed framework has a wide range of applications especially in the presence of adjacent structures of interest or under intra-structure inhomogeneities giving excellent quantitative results.
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Affiliation(s)
- Rafael Namías
- CIFASIS, UNR-CONICET/UAM (France), Bv 27 de febrero 210 bis, Rosario, Argentina.
| | - Juan Pablo D'Amato
- Consejo Nacional de Investigaciones Científicas y Técnicas and Instituto PLADEMA, Universidad Nacional del Centro, Tandil, Argentina
| | - Mariana Del Fresno
- Comisión de Investigaciones Científicas de la Prov. de Buenos Aires (CIC-PBA) and Instituto PLADEMA, Universidad Nacional del Centro, Tandil, Argentina
| | - Marcelo Vénere
- Comisión Nacional de Energía Atómica (CNEA) and Instituto PLADEMA, Universidad Nacional del Centro, Tandil, Argentina
| | - Nicola Pirró
- Digestive Surgery Department, Hôpital La Timone, Marseille, France
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Segmentation of uterine fibroid ultrasound images using a dynamic statistical shape model in HIFU therapy. Comput Med Imaging Graph 2015; 46 Pt 3:302-14. [PMID: 26459767 DOI: 10.1016/j.compmedimag.2015.07.004] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2014] [Revised: 06/24/2015] [Accepted: 07/13/2015] [Indexed: 11/20/2022]
Abstract
Segmenting the lesion areas from ultrasound (US) images is an important step in the intra-operative planning of high-intensity focused ultrasound (HIFU). However, accurate segmentation remains a challenge due to intensity inhomogeneity, blurry boundaries in HIFU US images and the deformation of uterine fibroids caused by patient's breathing or external force. This paper presents a novel dynamic statistical shape model (SSM)-based segmentation method to accurately and efficiently segment the target region in HIFU US images of uterine fibroids. For accurately learning the prior shape information of lesion boundary fluctuations in the training set, the dynamic properties of stochastic differential equation and Fokker-Planck equation are incorporated into SSM (referred to as SF-SSM). Then, a new observation model of lesion areas (named to RPFM) in HIFU US images is developed to describe the features of the lesion areas and provide a likelihood probability to the prior shape given by SF-SSM. SF-SSM and RPFM are integrated into active contour model to improve the accuracy and robustness of segmentation in HIFU US images. We compare the proposed method with four well-known US segmentation methods to demonstrate its superiority. The experimental results in clinical HIFU US images validate the high accuracy and robustness of our approach, even when the quality of the images is unsatisfactory, indicating its potential for practical application in HIFU therapy.
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Dolz J, Massoptier L, Vermandel M. Segmentation algorithms of subcortical brain structures on MRI for radiotherapy and radiosurgery: A survey. Ing Rech Biomed 2015. [DOI: 10.1016/j.irbm.2015.06.001] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
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23
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Zhao F, Xie X, Roach M. Computer Vision Techniques for Transcatheter Intervention. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2015; 3:1900331. [PMID: 27170893 PMCID: PMC4848047 DOI: 10.1109/jtehm.2015.2446988] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/13/2015] [Revised: 04/10/2015] [Accepted: 06/09/2015] [Indexed: 12/02/2022]
Abstract
Minimally invasive transcatheter technologies have demonstrated substantial promise for the diagnosis and the treatment of cardiovascular diseases. For example, transcatheter aortic valve implantation is an alternative to aortic valve replacement for the treatment of severe aortic stenosis, and transcatheter atrial fibrillation ablation is widely used for the treatment and the cure of atrial fibrillation. In addition, catheter-based intravascular ultrasound and optical coherence tomography imaging of coronary arteries provides important information about the coronary lumen, wall, and plaque characteristics. Qualitative and quantitative analysis of these cross-sectional image data will be beneficial to the evaluation and the treatment of coronary artery diseases such as atherosclerosis. In all the phases (preoperative, intraoperative, and postoperative) during the transcatheter intervention procedure, computer vision techniques (e.g., image segmentation and motion tracking) have been largely applied in the field to accomplish tasks like annulus measurement, valve selection, catheter placement control, and vessel centerline extraction. This provides beneficial guidance for the clinicians in surgical planning, disease diagnosis, and treatment assessment. In this paper, we present a systematical review on these state-of-the-art methods. We aim to give a comprehensive overview for researchers in the area of computer vision on the subject of transcatheter intervention. Research in medical computing is multi-disciplinary due to its nature, and hence, it is important to understand the application domain, clinical background, and imaging modality, so that methods and quantitative measurements derived from analyzing the imaging data are appropriate and meaningful. We thus provide an overview on the background information of the transcatheter intervention procedures, as well as a review of the computer vision techniques and methodologies applied in this area.
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Affiliation(s)
- Feng Zhao
- Department of Computer ScienceSwansea UniversitySwanseaSA2 8PPU.K.
| | - Xianghua Xie
- Department of Computer ScienceSwansea UniversitySwanseaSA2 8PPU.K.
| | - Matthew Roach
- Department of Computer ScienceSwansea UniversitySwanseaSA2 8PPU.K.
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24
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Zhao F, Liang J, Chen D, Wang C, Yang X, Chen X, Cao F. Automatic segmentation method for bone and blood vessel in murine hindlimb. Med Phys 2015; 42:4043-54. [DOI: 10.1118/1.4922200] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
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25
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Wu Z, Jiang X, Zheng N, Cheng D. Efficient block-wise temporally consistent contour extraction in image sequences. Neurocomputing 2015. [DOI: 10.1016/j.neucom.2014.11.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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26
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Brandes S, Mokhtari Z, Essig F, Hünniger K, Kurzai O, Figge MT. Automated segmentation and tracking of non-rigid objects in time-lapse microscopy videos of polymorphonuclear neutrophils. Med Image Anal 2015; 20:34-51. [DOI: 10.1016/j.media.2014.10.002] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2014] [Revised: 09/28/2014] [Accepted: 10/11/2014] [Indexed: 11/30/2022]
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27
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Retinal image registration using topological vascular tree segmentation and bifurcation structures. Biomed Signal Process Control 2015. [DOI: 10.1016/j.bspc.2014.10.009] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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28
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Zhou C, Chan HP, Chughtai A, Kuriakose J, Agarwal P, Kazerooni EA, Hadjiiski LM, Patel S, Wei J. Computerized analysis of coronary artery disease: performance evaluation of segmentation and tracking of coronary arteries in CT angiograms. Med Phys 2014; 41:081912. [PMID: 25086543 PMCID: PMC4111838 DOI: 10.1118/1.4890294] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2013] [Revised: 06/08/2014] [Accepted: 07/02/2014] [Indexed: 11/07/2022] Open
Abstract
PURPOSE The authors are developing a computer-aided detection system to assist radiologists in analysis of coronary artery disease in coronary CT angiograms (cCTA). This study evaluated the accuracy of the authors' coronary artery segmentation and tracking method which are the essential steps to define the search space for the detection of atherosclerotic plaques. METHODS The heart region in cCTA is segmented and the vascular structures are enhanced using the authors' multiscale coronary artery response (MSCAR) method that performed 3D multiscale filtering and analysis of the eigenvalues of Hessian matrices. Starting from seed points at the origins of the left and right coronary arteries, a 3D rolling balloon region growing (RBG) method that adapts to the local vessel size segmented and tracked each of the coronary arteries and identifies the branches along the tracked vessels. The branches are queued and subsequently tracked until the queue is exhausted. With Institutional Review Board approval, 62 cCTA were collected retrospectively from the authors' patient files. Three experienced cardiothoracic radiologists manually tracked and marked center points of the coronary arteries as reference standard following the 17-segment model that includes clinically significant coronary arteries. Two radiologists visually examined the computer-segmented vessels and marked the mistakenly tracked veins and noisy structures as false positives (FPs). For the 62 cases, the radiologists marked a total of 10191 center points on 865 visible coronary artery segments. RESULTS The computer-segmented vessels overlapped with 83.6% (8520/10191) of the center points. Relative to the 865 radiologist-marked segments, the sensitivity reached 91.9% (795/865) if a true positive is defined as a computer-segmented vessel that overlapped with at least 10% of the reference center points marked on the segment. When the overlap threshold is increased to 50% and 100%, the sensitivities were 86.2% and 53.4%, respectively. For the 62 test cases, a total of 55 FPs were identified by radiologist in 23 of the cases. CONCLUSIONS The authors' MSCAR-RBG method achieved high sensitivity for coronary artery segmentation and tracking. Studies are underway to further improve the accuracy for the arterial segments affected by motion artifacts, severe calcified and noncalcified soft plaques, and to reduce the false tracking of the veins and other noisy structures. Methods are also being developed to detect coronary artery disease along the tracked vessels.
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Affiliation(s)
- Chuan Zhou
- Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109
| | - Heang-Ping Chan
- Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109
| | - Aamer Chughtai
- Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109
| | - Jean Kuriakose
- Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109
| | - Prachi Agarwal
- Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109
| | - Ella A Kazerooni
- Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109
| | | | - Smita Patel
- Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109
| | - Jun Wei
- Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109
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Delibasis KK, Asvestas PA, Kechriniotis AI, Matsopoulos GK. An implicit evolution scheme for active contours and surfaces based on IIR filtering. Comput Biol Med 2014; 48:42-54. [DOI: 10.1016/j.compbiomed.2014.02.007] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2013] [Revised: 01/17/2014] [Accepted: 02/12/2014] [Indexed: 11/16/2022]
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30
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MESA: Complete approach for design and evaluation of segmentation methods using real and simulated tomographic images. Biocybern Biomed Eng 2014. [DOI: 10.1016/j.bbe.2014.02.003] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
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31
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Garcia MP, Velut J, Boulmier D, Leclercq C, Garreau M, Haigron P, Toumoulin C. Coronary vein extraction in MSCT volumes using minimum cost path and geometrical moments. IEEE J Biomed Health Inform 2013; 17:336-45. [PMID: 24235110 DOI: 10.1109/jbhi.2013.2245420] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
This work deals with the extraction of patient-specific coronary venous anatomy in preoperative multislice computed tomography (MSCT) volumes. A hybrid approach has been specifically designed for low-contrast vascular structure detection. It makes use of a minimum cost path technique with a Fast-Marching front propagation to extract the vessel centerline. A second procedure was applied to refine the position of the path and estimate the local radius along the vessel. This was achieved with an iterative multiscale algorithm based on geometrical moments. Parameter tuning was performed using a dedicated numerical phantom, and then the algorithm was applied to extract the coronary venous system. Results are provided on three MSCT volume sequences acquired for patients selected for a cardiac resynchronization therapy procedure. A visibility study was carried out by a medical expert who labeled venous segments on a set of 18 volumes. A comparison with two other Fast-Marching techniques and a geometrical moment based tracking method is also reported.
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32
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Improved algorithm for gradient vector flow based active contour model using global and local information. ScientificWorldJournal 2013; 2013:479675. [PMID: 24223506 PMCID: PMC3800599 DOI: 10.1155/2013/479675] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2013] [Accepted: 08/20/2013] [Indexed: 11/17/2022] Open
Abstract
Active contour models are used to extract object boundary from digital image, but there is poor convergence for the targets with deep concavities. We proposed an improved approach based on existing gradient vector flow methods. Main contributions of this paper are a new algorithm to determine the false part of active contour with higher accuracy from the global force of gradient vector flow and a new algorithm to update the external force field together with the local information of magnetostatic force. Our method has a semidynamic external force field, which is adjusted only when the false active contour exists. Thus, active contours have more chances to approximate the complex boundary, while the computational cost is limited effectively. The new algorithm is tested on irregular shapes and then on real images such as MRI and ultrasound medical data. Experimental results illustrate the efficiency of our method, and the computational complexity is also analyzed.
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33
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Bradshaw AP, Taubman DS, Todd MJ, Magnussen JS, Halmagyi GM. Augmented active surface model for the recovery of small structures in CT. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2013; 22:4394-4406. [PMID: 24048014 DOI: 10.1109/tip.2013.2273666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
This paper devises an augmented active surface model for the recovery of small structures in a low resolution and high noise setting, where the role of regularization is especially important. The emphasis here is on evaluating performance using real clinical computed tomography (CT) data with comparisons made to an objective ground truth acquired using micro-CT. In this paper, we show that the application of conventional active contour methods to small objects leads to non-optimal results because of the inherent properties of the energy terms and their interactions with one another. We show that the blind use of a gradient magnitude based energy performs poorly at these object scales and that the point spread function (PSF) is a critical factor that needs to be accounted for. We propose a new model that augments the external energy with prior knowledge by incorporating the PSF and the assumption of reasonably constant underlying CT numbers.
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34
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Delgado-Gonzalo R, Chenouard N, Unser M. Spline-based deforming ellipsoids for interactive 3D bioimage segmentation. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2013; 22:3926-3940. [PMID: 23708807 DOI: 10.1109/tip.2013.2264680] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
We present a new fast active-contour model (a.k.a. snake) for image segmentation in 3D microscopy. We introduce a parametric design that relies on exponential B-spline bases and allows us to build snakes that are able to reproduce ellipsoids. We design our bases to have the shortest-possible support, subject to some constraints. Thus, computational efficiency is maximized. The proposed 3D snake can approximate blob-like objects with good accuracy and can perfectly reproduce spheres and ellipsoids, irrespective of their position and orientation. The optimization process is remarkably fast due to the use of Gauss' theorem within our energy computation scheme. Our technique yields successful segmentation results, even for challenging data where object contours are not well defined. This is due to our parametric approach that allows one to favor prior shapes. In addition, this paper provides a software that gives full control over the snakes via an intuitive manipulation of few control points.
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Affiliation(s)
- Ricard Delgado-Gonzalo
- Biomedical Imaging Group, École polytechnique fédérale de Lausanne, Lausanne, Switzerland.
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35
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Incremental value of color coding in 3D volume rendered CT images for interpretation of complex cardiothoracic vascular malformations. Int J Cardiol 2013; 168:4692-8. [DOI: 10.1016/j.ijcard.2013.07.176] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2013] [Revised: 07/17/2013] [Accepted: 07/20/2013] [Indexed: 11/21/2022]
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36
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Computer-assisted system with multiple feature fused support vector machine for sperm morphology diagnosis. BIOMED RESEARCH INTERNATIONAL 2013; 2013:687607. [PMID: 24191249 PMCID: PMC3803132 DOI: 10.1155/2013/687607] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/15/2013] [Revised: 07/04/2013] [Accepted: 07/23/2013] [Indexed: 11/17/2022]
Abstract
Sperm morphology is an important technique in identifying the health of sperms. In this paper we present a new system and novel approaches to classify different kinds of sperm images in order to assess their health. Our approach mainly relies on a one-dimensional feature which is extracted from the sperm's contour with gray level information. Our approach can handle rotation and scaling of the image. Moreover, it is fused with SVM classification to improve its accuracy. In our evaluation, our method has better performance than the existing approaches to sperm classification.
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37
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Nakaguro Y, Dailey MN, Marukatat S, Makhanov SS. Defeating line-noise CAPTCHAs with multiple quadratic snakes. Comput Secur 2013. [DOI: 10.1016/j.cose.2013.05.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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38
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Park S, Min Lee S, Kim N, Beom Seo J, Shin H. Automatic reconstruction of the arterial and venous trees on volumetric chest CT. Med Phys 2013; 40:071906. [DOI: 10.1118/1.4811203] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
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39
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Maška M, Daněk O, Garasa S, Rouzaut A, Muñoz-Barrutia A, Ortiz-de-Solorzano C. Segmentation and shape tracking of whole fluorescent cells based on the Chan-Vese model. IEEE TRANSACTIONS ON MEDICAL IMAGING 2013; 32:995-1006. [PMID: 23372077 DOI: 10.1109/tmi.2013.2243463] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
We present a fast and robust approach to tracking the evolving shape of whole fluorescent cells in time-lapse series. The proposed tracking scheme involves two steps. First, coherence-enhancing diffusion filtering is applied on each frame to reduce the amount of noise and enhance flow-like structures. Second, the cell boundaries are detected by minimizing the Chan-Vese model in the fast level set-like and graph cut frameworks. To allow simultaneous tracking of multiple cells over time, both frameworks have been integrated with a topological prior exploiting the object indication function. The potential of the proposed tracking scheme and the advantages and disadvantages of both frameworks are demonstrated on 2-D and 3-D time-lapse series of rat adipose-derived mesenchymal stem cells and human lung squamous cell carcinoma cells, respectively.
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Affiliation(s)
- Martin Maška
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, 60200 Brno, Czech Republic.
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40
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He L, Long LR, Antani S, Thoma GR. Histology image analysis for carcinoma detection and grading. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2012; 107:538-56. [PMID: 22436890 PMCID: PMC3587978 DOI: 10.1016/j.cmpb.2011.12.007] [Citation(s) in RCA: 155] [Impact Index Per Article: 12.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/23/2010] [Revised: 09/27/2011] [Accepted: 12/13/2011] [Indexed: 05/25/2023]
Abstract
This paper presents an overview of the image analysis techniques in the domain of histopathology, specifically, for the objective of automated carcinoma detection and classification. As in other biomedical imaging areas such as radiology, many computer assisted diagnosis (CAD) systems have been implemented to aid histopathologists and clinicians in cancer diagnosis and research, which have been attempted to significantly reduce the labor and subjectivity of traditional manual intervention with histology images. The task of automated histology image analysis is usually not simple due to the unique characteristics of histology imaging, including the variability in image preparation techniques, clinical interpretation protocols, and the complex structures and very large size of the images themselves. In this paper we discuss those characteristics, provide relevant background information about slide preparation and interpretation, and review the application of digital image processing techniques to the field of histology image analysis. In particular, emphasis is given to state-of-the-art image segmentation methods for feature extraction and disease classification. Four major carcinomas of cervix, prostate, breast, and lung are selected to illustrate the functions and capabilities of existing CAD systems.
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Affiliation(s)
- Lei He
- National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD, USA.
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41
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Nakhmani A, Tannenbaum A. Self-crossing detection and location for parametric active contours. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2012; 21:3150-3156. [PMID: 22374361 PMCID: PMC3660981 DOI: 10.1109/tip.2012.2188808] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
Active contours are very popular tools for video tracking and image segmentation. Parameterized contours are used due to their fast evolution and have become the method of choice in the Sobolev context. Unfortunately, these contours are not easily adaptable to topological changes, and they may sometimes develop undesirable loops, resulting in erroneous results. To solve such topological problems, one needs an algorithm for contour self-crossing detection. We propose a simple methodology via simple techniques from differential topology. The detection is accomplished by inspecting the total net change of a given contour's angle, without point sorting and plane sweeping. We discuss the efficient implementation of the algorithm. We also provide algorithms for locating crossings by angle considerations and by plotting the four-connected lines between the discrete contour points. The proposed algorithms can be added to any parametric active-contour model. We show examples of successful tracking in real-world video sequences by Sobolev active contours and the proposed algorithms and provide ideas for further research.
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Affiliation(s)
- Arie Nakhmani
- Department of Electrical Engineering, Technion, Haifa 32000, Israel. He is now with the Department of Electrical and Computer Engineering, Boston University, Boston, MA 02115 USA
| | - Allen Tannenbaum
- Department of Electrical and Computer Engineering and Department of Biomedical Engineering, Boston University, Boston, MA 02115 USA, and is also with the Department of Radiology/Wallace Cancer Center, University of Alabama at Birmingham, Birmingham, AL 35294-1150
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42
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Delgado-Gonzalo R, Thévenaz P, Seelamantula CS, Unser M. Snakes with an ellipse-reproducing property. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2012; 21:1258-1271. [PMID: 21965208 DOI: 10.1109/tip.2011.2169975] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
We present a new class of continuously defined parametric snakes using a special kind of exponential splines as basis functions. We have enforced our bases to have the shortest possible support subject to some design constraints to maximize efficiency. While the resulting snakes are versatile enough to provide a good approximation of any closed curve in the plane, their most important feature is the fact that they admit ellipses within their span. Thus, they can perfectly generate circular and elliptical shapes. These features are appropriate to delineate cross sections of cylindrical-like conduits and to outline bloblike objects. We address the implementation details and illustrate the capabilities of our snake with synthetic and real data.
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Affiliation(s)
- Ricard Delgado-Gonzalo
- Biomedical Imaging Group, École polytechnique fédérale de Lausanne, Lausanne, Switzerland
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43
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Morra J, Tu Z, Toga A, Thompson P. Machine Learning for Brain Image Segmentation. Mach Learn 2012. [DOI: 10.4018/978-1-60960-818-7.ch408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
In this chapter, the authors review a variety of algorithms developed by different groups for automatically segmenting structures in medical images, such as brain MRI scans. Some of the simpler methods, based on active contours, deformable image registration, and anisotropic Markov random fields, have known weaknesses, which can be largely overcome by learning methods that better encode knowledge on anatomical variability. The authors show how the anatomical segmentation problem may be re-cast in a Bayesian framework. They then present several different learning techniques increasing in complexity until they derive two algorithms recently proposed by the authors. The authors show how these automated algorithms are validated empirically, by comparison with segmentations by experts, which serve as independent ground truth, and in terms of their power to detect disease effects in Alzheimer’s disease. They show how these methods can be used to investigate factors that influence disease progression in databases of thousands of images. Finally the authors indicate some promising directions for future work.
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Affiliation(s)
| | - Zhuowen Tu
- University of California Los Angeles, USA
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44
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Contour detection and completion for inpainting and segmentation based on topological gradient and fast marching algorithms. Int J Biomed Imaging 2011; 2011:592924. [PMID: 22194734 PMCID: PMC3238395 DOI: 10.1155/2011/592924] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2011] [Revised: 09/12/2011] [Accepted: 09/12/2011] [Indexed: 11/23/2022] Open
Abstract
We combine in this paper the topological gradient, which is a powerful method for edge detection in image processing, and a variant of the minimal path method in order to find connected contours. The topological gradient provides a more global analysis of the image than the standard gradient and identifies the main edges of an image. Several image processing problems (e.g., inpainting and segmentation) require continuous contours. For this purpose, we consider the fast marching algorithm in order to find minimal paths in the topological gradient image. This coupled algorithm quickly provides accurate and connected contours. We present then two numerical applications, to image inpainting and segmentation, of this hybrid algorithm.
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BOUFAMA BS, O'CONNELL DJ. IDENTIFICATION AND MATCHING OF PLANES IN A PAIR OF UNCALIBRATED IMAGES. INT J PATTERN RECOGN 2011. [DOI: 10.1142/s0218001403002794] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
In this paper, we propose a new method to simultaneously achieve segmentation and dense matching in a pair of stereo images. In contrast to conventional methods that are based on similarity or correlation techniques, this method is based on geometry, and uses correlations only on a limited number of key points. Stemming from the observation that our environment is abundant in planes, this method focuses on segmentation and matching of planes in an observed scene. Neither prior knowledge about the scene nor camera calibration are needed. Using two uncalibrated images as inputs, the method starts with a rough identification of a potential plane, defined by three points only. Based on these three points, a plane homography is then calculated and, used for validation. Starting from a seed region defined by the original three points, the method grows the current region by successive move/confirmation steps until occlusions and/or surface discontinuity occur. In this case, the homography-based mapping of points between the two images will not be valid anymore. This condition is detected by the correlation, used in the confirmation process. In particular, this method grows a region even across different colors as long as the region is planar. Experiments on real images validated our method and showed its capability and performance.
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Affiliation(s)
- B. S. BOUFAMA
- School of Computer Science, University of Windsor, Windsor, Ontario, N9B 3P4, Canada
| | - D. J. O'CONNELL
- School of Computer Science, University of Windsor, Windsor, Ontario, N9B 3P4, Canada
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A framework of vertebra segmentation using the active shape model-based approach. Int J Biomed Imaging 2011; 2011:621905. [PMID: 21826134 PMCID: PMC3149802 DOI: 10.1155/2011/621905] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2010] [Revised: 04/06/2011] [Accepted: 05/06/2011] [Indexed: 11/17/2022] Open
Abstract
We propose a medical image segmentation approach based on the Active Shape Model theory. We apply this method for cervical vertebra detection. The main advantage of this approach is the application of a statistical model created after a training stage. Thus, the knowledge and interaction of the domain expert intervene in this approach. Our application allows the use of two different models, that is, a global one (with several vertebrae) and a local one (with a single vertebra). Two modes of segmentation are also proposed: manual and semiautomatic. For the manual mode, only two points are selected by the user on a given image. The first point needs to be close to the lower anterior corner of the last vertebra and the second near the upper anterior corner of the first vertebra. These two points are required to initialize the segmentation process. We propose to use the Harris corner detector combined with three successive filters to carry out the semiautomatic process. The results obtained on a large set of X-ray images are very promising.
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Yotter RA, Dahnke R, Thompson PM, Gaser C. Topological correction of brain surface meshes using spherical harmonics. Hum Brain Mapp 2011; 32:1109-24. [PMID: 20665722 PMCID: PMC6869946 DOI: 10.1002/hbm.21095] [Citation(s) in RCA: 128] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2009] [Revised: 03/23/2010] [Accepted: 04/19/2010] [Indexed: 11/06/2022] Open
Abstract
Surface reconstruction methods allow advanced analysis of structural and functional brain data beyond what can be achieved using volumetric images alone. Automated generation of cortical surface meshes from 3D brain MRI often leads to topological defects and geometrical artifacts that must be corrected to permit subsequent analysis. Here, we propose a novel method to repair topological defects using a surface reconstruction that relies on spherical harmonics. First, during reparameterization of the surface using a tiled platonic solid, the original MRI intensity values are used as a basis to select either a "fill" or "cut" operation for each topological defect. We modify the spherical map of the uncorrected brain surface mesh, such that certain triangles are favored while searching for the bounding triangle during reparameterization. Then, a low-pass filtered alternative reconstruction based on spherical harmonics is patched into the reconstructed surface in areas that previously contained defects. Self-intersections are repaired using a local smoothing algorithm that limits the number of affected points to less than 0.1% of the total, and as a last step, all modified points are adjusted based on the T1 intensity. We found that the corrected reconstructions have reduced distance error metrics compared with a "gold standard" surface created by averaging 12 scans of the same brain. Ninety-three percent of the topological defects in a set of 10 scans of control subjects were accurately corrected. The entire process takes 6-8 min of computation time. Further improvements are discussed, especially regarding the use of the T1-weighted image to make corrections.
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Dufour A, Thibeaux R, Labruyère E, Guillén N, Olivo-Marin JC. 3-D active meshes: fast discrete deformable models for cell tracking in 3-D time-lapse microscopy. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2011; 20:1925-1937. [PMID: 21193379 DOI: 10.1109/tip.2010.2099125] [Citation(s) in RCA: 79] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
Variational deformable models have proven over the past decades a high efficiency for segmentation and tracking in 2-D sequences. Yet, their application to 3-D time-lapse images has been hampered by discretization issues, heavy computational loads and lack of proper user visualization and interaction, limiting their use for routine analysis of large data-sets. We propose here to address these limitations by reformulating the problem entirely in the discrete domain using 3-D active meshes, which express a surface as a discrete triangular mesh, and minimize the energy functional accordingly. By performing computations in the discrete domain, computational costs are drastically reduced, whilst the mesh formalism allows to benefit from real-time 3-D rendering and other GPU-based optimizations. Performance evaluations on both simulated and real biological data sets show that this novel framework outperforms current state-of-the-art methods, constituting a light and fast alternative to traditional variational models for segmentation and tracking applications.
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Affiliation(s)
- Alexandre Dufour
- Institut Pasteur, Quantitative Image Analysis Unit, Paris, France
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Zhou C, Chan HP, Chughtai A, Patel S, Hadjiiski LM, Wei J, Kazerooni EA. Automated coronary artery tree extraction in coronary CT angiography using a multiscale enhancement and dynamic balloon tracking (MSCAR-DBT) method. Comput Med Imaging Graph 2011; 36:1-10. [PMID: 21601422 DOI: 10.1016/j.compmedimag.2011.04.001] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2010] [Revised: 04/01/2011] [Accepted: 04/01/2011] [Indexed: 11/17/2022]
Abstract
RATIONAL AND OBJECTIVES To evaluate our prototype method for segmentation and tracking of the coronary arterial tree, which is the foundation for a computer-aided detection (CADe) system to be developed to assist radiologists in detecting non-calcified plaques in coronary CT angiography (cCTA) scans. MATERIALS AND METHODS The heart region was first extracted by a morphological operation and an adaptive thresholding method based on expectation-maximization (EM) estimation. The vascular structures within the heart region were enhanced and segmented using a multiscale coronary response (MSCAR) method that combined 3D multiscale filtering, analysis of the eigenvalues of Hessian matrices and EM estimation segmentation. After the segmentation of vascular structures, the coronary arteries were tracked by a 3D dynamic balloon tracking (DBT) method. The DBT method started at two manually identified seed points located at the origins of the left and right coronary arteries (LCA and RCA) for extraction of the arterial trees. The coronary arterial trees of a data set containing 20 ECG-gated contrast-enhanced cCTA scans were extracted by our MSCAR-DBT method and a clinical GE Advantage workstation. Two experienced thoracic radiologists visually examined the coronary arteries on the original cCTA scans and the rendered volume of segmented vessels to count the untracked false-negative (FN) segments and false positives (FPs) for both methods. RESULTS For the visible coronary arterial segments in the 20 cases, the radiologists identified that 25 segments were missed by our MSCAR-DBT method, ranging from 0 to 5 FN segments in individual cases, and that 55 artery segments were missed by the GE software, ranging from 0 to 7 FN segments in individual cases. 19 and 15 FPs were identified in our and the GE coronary trees, ranging from 0 to 4 FPs for both methods in individual cases, respectively. CONCLUSION The preliminary study demonstrates the feasibility of our MSCAR-DBT method for segmentation and tracking coronary artery trees. The results indicated that both our method and GE software can extract coronary artery trees reasonably well and the performance of our method is superior to that of GE software in this small data set. Further studies are underway to develop methods for improvement of the segmentation and tracking accuracy.
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Affiliation(s)
- Chuan Zhou
- Department of Radiology, University of Michigan, Ann Arbor 48109, USA.
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Truc PTH, Kim TS, Lee S, Lee YK. Homogeneity- and density distance-driven active contours for medical image segmentation. Comput Biol Med 2011; 41:292-301. [DOI: 10.1016/j.compbiomed.2011.03.006] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2010] [Revised: 11/10/2010] [Accepted: 03/18/2011] [Indexed: 10/18/2022]
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