1
|
Zhang Z, Han J, Ji W, Lou H, Li Z, Hu Y, Wang M, Qi B, Liu S. Improved deep learning for automatic localisation and segmentation of rectal cancer on T2-weighted MRI. J Med Radiat Sci 2024. [PMID: 38654675 DOI: 10.1002/jmrs.794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Accepted: 04/09/2024] [Indexed: 04/26/2024] Open
Abstract
INTRODUCTION The automatic segmentation approaches of rectal cancer from magnetic resonance imaging (MRI) are very valuable to relieve physicians from heavy workloads and enhance working efficiency. This study aimed to compare the segmentation accuracy of a proposed model with the other three models and the inter-observer consistency. METHODS A total of 65 patients with rectal cancer who underwent MRI examination were enrolled in our cohort and were randomly divided into a training cohort (n = 45) and a validation cohort (n = 20). Two experienced radiologists independently segmented rectal cancer lesions. A novel segmentation model (AttSEResUNet) was trained on T2WI based on ResUNet and attention mechanisms. The segmentation performance of the AttSEResUNet, U-Net, ResUNet and U-Net with Attention Gate (AttUNet) was compared, using Dice similarity coefficient (DSC), Hausdorff distance (HD), mean distance to agreement (MDA) and Jaccard index. The segmentation variability of automatic segmentation models and inter-observer was also evaluated. RESULTS The AttSEResUNet with post-processing showed perfect lesion recognition rate (100%) and false recognition rate (0), and its evaluation metrics outperformed other three models for two independent readers (observer 1: DSC = 0.839 ± 0.112, HD = 9.55 ± 6.68, MDA = 0.556 ± 0.722, Jaccard index = 0.736 ± 0.150; observer 2: DSC = 0.856 ± 0.099, HD = 11.0 ± 10.1, MDA = 0.789 ± 1.07, Jaccard index = 0.673 ± 0.130). The segmentation performance of AttSEResUNet was comparable and similar to manual variability (DSC = 0.857 ± 0.115, HD = 10.0 ± 10.0, MDA = 0.704 ± 1.17, Jaccard index = 0.666 ± 0.139). CONCLUSION Comparing with other three models, the proposed AttSEResUNet model was demonstrated as a more accurate model for contouring the rectal tumours in axial T2WI images, whose variability was similar to that of inter-observer.
Collapse
Affiliation(s)
- Zaixian Zhang
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Junqi Han
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Weina Ji
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Henan Lou
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Zhiming Li
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Yabin Hu
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Mingjia Wang
- College of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao, China
| | - Baozhu Qi
- College of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao, China
| | - Shunli Liu
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| |
Collapse
|
2
|
Huang X, Wang S, Gao X, Luo D, Xu W, Pang H, Zhou M. An H-GrabCut Image Segmentation Algorithm for Indoor Pedestrian Background Removal. Sensors (Basel) 2023; 23:7937. [PMID: 37765994 PMCID: PMC10536006 DOI: 10.3390/s23187937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 09/08/2023] [Accepted: 09/11/2023] [Indexed: 09/29/2023]
Abstract
In the context of predicting pedestrian trajectories for indoor mobile robots, it is crucial to accurately measure the distance between indoor pedestrians and robots. This study aims to address this requirement by extracting pedestrians as regions of interest and mitigating issues related to inaccurate depth camera distance measurements and illumination conditions. To tackle these challenges, we focus on an improved version of the H-GrabCut image segmentation algorithm, which involves four steps for segmenting indoor pedestrians. Firstly, we leverage the YOLO-V5 object recognition algorithm to construct detection nodes. Next, we propose an enhanced BIL-MSRCR algorithm to enhance the edge details of pedestrians. Finally, we optimize the clustering features of the GrabCut algorithm by incorporating two-dimensional entropy, UV component distance, and LBP texture feature values. The experimental results demonstrate that our algorithm achieves a segmentation accuracy of 97.13% in both the INRIA dataset and real-world tests, outperforming alternative methods in terms of sensitivity, missegmentation rate, and intersection-over-union metrics. These experiments confirm the feasibility and practicality of our approach. The aforementioned findings will be utilized in the preliminary processing of indoor mobile robot pedestrian trajectory prediction and enable path planning based on the predicted results.
Collapse
Affiliation(s)
- Xuchao Huang
- School of Automation, Guangxi University of Science and Technology, Liuzhou 545000, China
- Key Laboratory of Intelligent Sensing and Control, Liuzhou 545000, China
| | - Shigang Wang
- School of Automation, Guangxi University of Science and Technology, Liuzhou 545000, China
- Key Laboratory of Intelligent Sensing and Control, Liuzhou 545000, China
| | - Xueshan Gao
- School of Automation, Guangxi University of Science and Technology, Liuzhou 545000, China
- Key Laboratory of Intelligent Sensing and Control, Liuzhou 545000, China
| | - Dingji Luo
- Mechanical and Electrical College, Beijing Institute of Technology, Beijing 100190, China
| | - Weiye Xu
- School of Automation, Guangxi University of Science and Technology, Liuzhou 545000, China
- Key Laboratory of Intelligent Sensing and Control, Liuzhou 545000, China
| | - Huiqing Pang
- School of Automation, Guangxi University of Science and Technology, Liuzhou 545000, China
- Key Laboratory of Intelligent Sensing and Control, Liuzhou 545000, China
| | - Ming Zhou
- Hangke Jinggong Co., Ltd., Beijing 102400, China
| |
Collapse
|
3
|
Li L, Liu H, Li Q, Tian Z, Li Y, Geng W, Wang S. Near-Infrared Blood Vessel Image Segmentation Using Background Subtraction and Improved Mathematical Morphology. Bioengineering (Basel) 2023; 10:726. [PMID: 37370657 DOI: 10.3390/bioengineering10060726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 05/31/2023] [Accepted: 06/06/2023] [Indexed: 06/29/2023] Open
Abstract
The precise display of blood vessel information for doctors is crucial. This is not only true for facilitating intravenous injections, but also for the diagnosis and analysis of diseases. Currently, infrared cameras can be used to capture images of superficial blood vessels. However, their imaging quality always has the problems of noises, breaks, and uneven vascular information. In order to overcome these problems, this paper proposes an image segmentation algorithm based on the background subtraction and improved mathematical morphology. The algorithm regards the image as a superposition of blood vessels into the background, removes the noise by calculating the size of connected domains, achieves uniform blood vessel width, and smooths edges that reflect the actual blood vessel state. The algorithm is evaluated subjectively and objectively in this paper to provide a basis for vascular image quality assessment. Extensive experimental results demonstrate that the proposed method can effectively extract accurate and clear vascular information.
Collapse
Affiliation(s)
- Ling Li
- Beijing Engineerin Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Haoting Liu
- Beijing Engineerin Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Qing Li
- Beijing Engineerin Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Zhen Tian
- Beijing Engineerin Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Yajie Li
- Beijing Engineerin Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Wenjia Geng
- Department of Traditional Chinese Medicine, Peking University People's Hospital, Beijing 100044, China
| | - Song Wang
- Department of Nephrology, Peking University Third Hospital, Beijing 100191, China
| |
Collapse
|
4
|
Ayikpa KJ, Mamadou D, Ballo AB, Yao K, Gouton P, Adou KJ. CocoaMFDB: A dataset of cocoa pod maturity and families in an uncontrolled environment in Côte d'Ivoire. Data Brief 2023; 48:109196. [PMID: 37234732 PMCID: PMC10206420 DOI: 10.1016/j.dib.2023.109196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 04/17/2023] [Accepted: 04/27/2023] [Indexed: 05/28/2023] Open
Abstract
Cocoa cultivation is the basis for chocolate production; it has a unique aroma that makes it useful in the production of snacks and usable for cooking or baking. The maximum harvest period of cocoa is normally once or twice a year and spread over several months, depending on the country. Determining the best harvesting period for cocoa pods plays a major role in the export process and the pods quality. The degree of ripening of the pods affects the quality of the resulting beans. Also, unripe pods do not have enough sugar and may prevent proper bean fermentation. As for too-mature pods, they are usually dry, and their beans may germinate inside the pods, or they may develop a fungal disease and cannot be used. Computer-based determination of the ripeness of cocoa pods throughout image analysis could facilitate massive cocoa ripeness detection. Recent technological advances in computing power, communication systems, and machine learning techniques provide opportunities for agricultural engineering and computer scientists to meet the demands of the manual. The need for diverse and representative sets of pod images is essential for developing and testing automatic cocoa pod maturity detection systems. In this perspective, we collected images of cocoa pods to set up a database of cocoa pods of the Côte d'Ivoire named CocoaMFDB. We performed a pre-processing step using the CLAHE algorithm to improve the quality of the images since the effect of the light was not controlled on our data set. CocoaMFDB allows the characterization of cocoa pods according to their maturity level and provides information on the pod family for each image. Our dataset comprises three large families, namely Amelonado, Angoleta, and Guiana, grouped into two maturity categories: the ripe and unripe pods. It is, therefore, perfect for developing and evaluating image analysis algorithms for future research.
Collapse
Affiliation(s)
- Kacoutchy Jean Ayikpa
- ImVia, Université Bourgogne Franche-Comté, Dijon, France
- LaMI, Université Felix Houphouët-Boigny, Abidjan, Côte d'Ivoire
- UREN, Université Virtuelle de Côte d'ivoire, Abidjan, Côte d'Ivoire
| | - Diarra Mamadou
- LaMI, Université Felix Houphouët-Boigny, Abidjan, Côte d'Ivoire
| | | | - Konan Yao
- LaMI, Université Felix Houphouët-Boigny, Abidjan, Côte d'Ivoire
| | - Pierre Gouton
- ImVia, Université Bourgogne Franche-Comté, Dijon, France
| | | |
Collapse
|
5
|
C P, R JK. Retinal image enhancement based on color dominance of image. Sci Rep 2023; 13:7172. [PMID: 37138000 PMCID: PMC10156681 DOI: 10.1038/s41598-023-34212-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 04/26/2023] [Indexed: 05/05/2023] Open
Abstract
Real-time fundus images captured to detect multiple diseases are prone to different quality issues like illumination, noise, etc., resulting in less visibility of anomalies. So, enhancing the retinal fundus images is essential for a better prediction rate of eye diseases. In this paper, we propose Lab color space-based enhancement techniques for retinal image enhancement. Existing research works does not consider the relation between color spaces of the fundus image in selecting a specific channel to perform retinal image enhancement. Our unique contribution to this research work is utilizing the color dominance of an image in quantifying the distribution of information in the blue channel and performing enhancement in Lab space followed by a series of steps to optimize overall brightness and contrast. The test set of the Retinal Fundus Multi-disease Image Dataset is used to evaluate the performance of the proposed enhancement technique in identifying the presence or absence of retinal abnormality. The proposed technique achieved an accuracy of 89.53 percent.
Collapse
Affiliation(s)
- Priyadharsini C
- School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamilnadu, 600127, India
| | - Jagadeesh Kannan R
- School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamilnadu, 600127, India.
| |
Collapse
|
6
|
Mehdizadeh M, Tavakoli Tafti K, Soltani P. Evaluation of histogram equalization and contrast limited adaptive histogram equalization effect on image quality and fractal dimensions of digital periapical radiographs. Oral Radiol 2023; 39:418-424. [PMID: 36076131 DOI: 10.1007/s11282-022-00654-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Accepted: 08/31/2022] [Indexed: 11/29/2022]
Abstract
OBJECTIVES This study aims to evaluate the effects of histogram equalization (HE) and contrast limited adaptive histogram equalization (CLAHE) on periapical images and fractal dimensions in the periapical region. METHODS In this cross-sectional study, digital periapical images were selected from the archive of Dentistry School of Isfahan University of Medical Sciences. The radiographs were taken from mandibular and maxillary anterior single root teeth with healthy root and periodontium. After applying HE and CLAHE algorithms to images, two radiologists evaluated the quality of apex detection from using a 5-point Likert scale (from 5 for very good image quality to 1 for very bad image quality). Afterward, all the images were imported to the ImageJ application, and the region of interest (ROI) was specified as the region between the two central incisors. The fractal box-counting method was used to determine fractal dimensions (FD) values. Nonparametric Wilcoxon-Friedman test, Intraclass Correlation Coefficient test, T-test, and Pair T-test were performed as statistical analysis (α = 0.05). RESULTS Fifty-three radiographs were analyzed and the image quality assessments were significantly different between raw images and images after performing HE, CLAHE (p value < 0.001), and using CLAHE algorithm significantly increases image quality assessments more than HE (p value = 0.009). There was a significant difference in FD values for images after applying CLAHE and HE compared to raw images (p value < 0.001), and HE decreased the FD value significantly more than CLAHE (p value = 0.019). CONCLUSIONS Employing CLAHE and HE algorithm via OpenCV python library improves the periapical image quality, which is more significant using the CLAHE algorithm. Moreover, applying CLAHE and HE reduces trabecular bone structure detection and FD values in periapical images, especially in HE.
Collapse
Affiliation(s)
- Mojdeh Mehdizadeh
- Department of Oral and Maxillofacial Radiology, Dental Implants Research Center, Dental Research Institute, School of Dentistry, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Kioumars Tavakoli Tafti
- Dental Students' Research Committee, School of Dentistry, Isfahan University of Medical Sciences, Isfahan, Iran.
| | - Parisa Soltani
- Department of Oral and Maxillofacial Radiology, Dental Implants Research Center, Dental Research Institute, School of Dentistry, Isfahan University of Medical Sciences, Isfahan, Iran
| |
Collapse
|
7
|
Wu Z, Tan H, Luo J, Liang J, Lin J, Huang A, Li X, Wu Y. Hybrid enhancement algorithm for nailfold images with large fields of view. Microvasc Res 2023; 146:104472. [PMID: 36572207 DOI: 10.1016/j.mvr.2022.104472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 11/22/2022] [Accepted: 12/19/2022] [Indexed: 12/24/2022]
Abstract
Collecting and analyzing human nailfold images is an important component of studying human microcirculation. However, the large-field-of-view and high-resolution nailfold images captured by research microscopes introduce issues such as uneven brightness, low imaging contrast, and unclear vascular contours. To overcome these issues, this paper proposes a hybrid enhancement algorithm for nailfold images with large fields of view. First, adaptive histogram equalization with limited contrast (Clahe) is used to redistribute gray levels to enhance the brightness and contrast of images. Next, nonlocal means denoising (NL-means) is used to remove the noise amplified by Clahe algorithm. Finally, unsharp masking (Usm) is used to enhance the edge contour information of nailfold blood vessels. Comparing the enhanced images reveals that the hybrid enhancement algorithm improves the brightness and contrast of the nailfold image, makes the nailfold vessel contour more obvious, and the image noise continues to remain small, and it obtains the best visual effect. It is superior to other algorithms in terms of objective indicators and subjective evaluation.
Collapse
Affiliation(s)
- Zhiwei Wu
- School of Physics and Optoelectronic Engineering, Foshan University, Foshan 528000, China
| | - Haishu Tan
- School of Physics and Optoelectronic Engineering, Foshan University, Foshan 528000, China
| | - Jiaxiong Luo
- School of Physics and Optoelectronic Engineering, Foshan University, Foshan 528000, China
| | - Junzhao Liang
- School of Physics and Optoelectronic Engineering, Foshan University, Foshan 528000, China
| | - Jianan Lin
- School of Physics and Optoelectronic Engineering, Foshan University, Foshan 528000, China
| | - An Huang
- School of Physics and Optoelectronic Engineering, Foshan University, Foshan 528000, China
| | - Xiaosong Li
- School of Physics and Optoelectronic Engineering, Foshan University, Foshan 528000, China
| | - Yanxiong Wu
- School of Physics and Optoelectronic Engineering, Foshan University, Foshan 528000, China; Ji Hua Laboratory, Foshan, Guangdong 528200, China.
| |
Collapse
|
8
|
Sule OO, Ezugwu AE. A two-stage histogram equalization enhancement scheme for feature preservation in retinal fundus images. Biomed Signal Process Control 2023; 80:104384. [DOI: 10.1016/j.bspc.2022.104384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
|
9
|
Aziz T, Charoenlarpnopparut C, Mahapakulchai S. Deep learning-based hemorrhage detection for diabetic retinopathy screening. Sci Rep 2023; 13:1479. [PMID: 36707608 DOI: 10.1038/s41598-023-28680-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 01/23/2023] [Indexed: 01/29/2023] Open
Abstract
Diabetic retinopathy is a retinal compilation that causes visual impairment. Hemorrhage is one of the pathological symptoms of diabetic retinopathy that emerges during disease development. Therefore, hemorrhage detection reveals the presence of diabetic retinopathy in the early phase. Diagnosing the disease in its initial stage is crucial to adopt proper treatment so the repercussions can be prevented. The automatic deep learning-based hemorrhage detection method is proposed that can be used as the second interpreter for ophthalmologists to reduce the time and complexity of conventional screening methods. The quality of the images was enhanced, and the prospective hemorrhage locations were estimated in the preprocessing stage. Modified gamma correction adaptively illuminates fundus images by using gradient information to address the nonuniform brightness levels of images. The algorithm estimated the locations of potential candidates by using a Gaussian match filter, entropy thresholding, and mathematical morphology. The required objects were segmented using the regional diversity at estimated locations. The novel hemorrhage network is propounded for hemorrhage classification and compared with the renowned deep models. Two datasets benchmarked the model's performance using sensitivity, specificity, precision, and accuracy metrics. Despite being the shallowest network, the proposed network marked competitive results than LeNet-5, AlexNet, ResNet50, and VGG-16. The hemorrhage network was assessed using training time and classification accuracy through synthetic experimentation. Results showed promising accuracy in the classification stage while significantly reducing training time. The research concluded that increasing deep network layers does not guarantee good results but rather increases training time. The suitable architecture of a deep model and its appropriate parameters are critical for obtaining excellent outcomes.
Collapse
|
10
|
Vaiyapuri T, Jothi A, Narayanasamy K, Kamatchi K, Kadry S, Kim J. Design of a Honey Badger Optimization Algorithm with a Deep Transfer Learning-Based Osteosarcoma Classification Model. Cancers (Basel) 2022; 14:cancers14246066. [PMID: 36551552 PMCID: PMC9776881 DOI: 10.3390/cancers14246066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Revised: 11/30/2022] [Accepted: 12/04/2022] [Indexed: 12/14/2022] Open
Abstract
Osteosarcoma is one of the aggressive bone tumors with numerous histologic patterns. Histopathological inspection is a crucial criterion in the medical diagnosis of Osteosarcoma. Due to the advancement of computing power and hardware technology, pathological image analysis system based on artificial intelligence (AI) were more commonly used. But classifying many intricate pathology images by hand will be challenging for pathologists. The lack of labeling data makes the system difficult to build and costly. This article designs a Honey Badger Optimization with Deep Learning based Automated Osteosarcoma Classification (HBODL-AOC) model. The HBODL-AOC technique's goal is to identify osteosarcoma's existence using medical images. In the presented HBODL-AOC technique, image preprocessing is initially performed by contrast enhancement technique. For feature extraction, the HBODL-AOC technique employs a deep convolutional neural network-based Mobile networks (MobileNet) model with an Adam optimizer for hyperparameter tuning. Finally, the adaptive neuro-fuzzy inference system (ANFIS) approach is implemented for the HBO (Honey Badger Optimization) algorithm can tune osteosarcoma classification and the membership function (MF). To demonstrate the enhanced classification performance of the HBODL-AOC approach, a sequence of simulations was performed. The extensive simulation analysis portrayed the improved performance of the HBODL-AOC technique over existing DL models.
Collapse
Affiliation(s)
- Thavavel Vaiyapuri
- Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al Kharj 16278, Saudi Arabia
| | - Akshya Jothi
- Department of Computational Intelligence, SRM Institute of Science and Technology, Kancheepuram 603203, India
| | - Kanagaraj Narayanasamy
- Department of Computer Science, Karpagam Academy of Higher Education, Coimbatore 641021, India
| | - Kartheeban Kamatchi
- Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education, Krishnankoil 626126, India
| | - Seifedine Kadry
- Department of Applied Data Science, Noroff University College, 4612 Kristiansand, Norway
- Artificial Intelligence Research Center (AIRC), Ajman University, Ajman 346, United Arab Emirates
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos P.O. Box 13-5053, Lebanon
| | - Jungeun Kim
- Department of Software, Kongju National University, Cheonan 31080, Republic of Korea
- Correspondence:
| |
Collapse
|
11
|
Xue Y, Zhu J, Huang X, Xu X, Li X, Zheng Y, Zhu Z, Jin K, Ye J, Gong W, Si K. A multi-feature deep learning system to enhance glaucoma severity diagnosis with high accuracy and fast speed. J Biomed Inform 2022; 136:104233. [DOI: 10.1016/j.jbi.2022.104233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Revised: 09/21/2022] [Accepted: 10/17/2022] [Indexed: 11/06/2022]
|
12
|
Toptaş B, Hanbay D. Separation of arteries and veins in retinal fundus images with a new CNN architecture. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 2022. [DOI: 10.1080/21681163.2022.2151066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Buket Toptaş
- Computer Engineering Department, Engineering and Natural Science Faculty, Bandırma Onyedi Eylül University, Balıkesir, Turkey
| | - Davut Hanbay
- Computer Engineering Department, Engineering Faculty, Inonu University, Malatya, Turkey
| |
Collapse
|
13
|
Aziz T, Charoenlarpnopparut C, Mahapakulchai S. Comparing Conventional and Deep Feature Models for Classifying Fundus Photography of Hemorrhages. Journal of Healthcare Engineering 2022; 2022:1-9. [DOI: 10.1155/2022/7387174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 03/27/2022] [Accepted: 04/08/2022] [Indexed: 11/20/2022]
Abstract
Diabetic retinopathy is an eye-related pathology creating abnormalities and causing visual impairment, proper treatment of which requires identifying irregularities. This research uses a hemorrhage detection method and compares the classification of conventional and deep features. Especially, the method identifies hemorrhage connected with blood vessels or residing at the retinal border and was reported challenging. Initially, adaptive brightness adjustment and contrast enhancement rectify degraded images. Prospective locations of hemorrhages are estimated by a Gaussian matched filter, entropy thresholding, and morphological operation. Hemorrhages are segmented by a novel technique based on the regional variance of intensities. Features are then extracted by conventional methods and deep models for training support vector machines and the results are evaluated. Evaluation metrics for each model are promising, but findings suggest that comparatively, deep models are more effective than conventional features.
Collapse
|
14
|
Alrowais F, S. Alotaibi S, Marzouk R, S. Salama A, Rizwanullah M, Zamani AS, Atta Abdelmageed A, I. Eldesouki M. Manta Ray Foraging Optimization Transfer Learning-Based Gastric Cancer Diagnosis and Classification on Endoscopic Images. Cancers (Basel) 2022; 14:cancers14225661. [PMID: 36428752 PMCID: PMC9688577 DOI: 10.3390/cancers14225661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 11/10/2022] [Accepted: 11/14/2022] [Indexed: 11/19/2022] Open
Abstract
Gastric cancer (GC) diagnoses using endoscopic images have gained significant attention in the healthcare sector. The recent advancements of computer vision (CV) and deep learning (DL) technologies pave the way for the design of automated GC diagnosis models. Therefore, this study develops a new Manta Ray Foraging Optimization Transfer Learning technique that is based on Gastric Cancer Diagnosis and Classification (MRFOTL-GCDC) using endoscopic images. For enhancing the quality of the endoscopic images, the presented MRFOTL-GCDC technique executes the Wiener filter (WF) to perform a noise removal process. In the presented MRFOTL-GCDC technique, MRFO with SqueezeNet model is used to derive the feature vectors. Since the trial-and-error hyperparameter tuning is a tedious process, the MRFO algorithm-based hyperparameter tuning results in enhanced classification results. Finally, the Elman Neural Network (ENN) model is utilized for the GC classification. To depict the enhanced performance of the presented MRFOTL-GCDC technique, a widespread simulation analysis is executed. The comparison study reported the improvement of the MRFOTL-GCDC technique for endoscopic image classification purposes with an improved accuracy of 99.25%.
Collapse
Affiliation(s)
- Fadwa Alrowais
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Saud S. Alotaibi
- Department of Information Systems, College of Computing and Information System, Umm Al-Qura University, Saudi Arabia
| | - Radwa Marzouk
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Ahmed S. Salama
- Department of Electrical Engineering, Faculty of Engineering & Technology, Future University in Egypt, New Cairo 11845, Egypt
| | - Mohammed Rizwanullah
- Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia
- Correspondence:
| | - Abu Sarwar Zamani
- Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia
| | - Amgad Atta Abdelmageed
- Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia
| | - Mohamed I. Eldesouki
- Department of Information System, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia
| |
Collapse
|
15
|
Zhang D, Li R, Lou X, Luo J. Hessian filter-assisted full diameter at half maximum (FDHM) segmentation and quantification method for optical-resolution photoacoustic microscopy. Biomed Opt Express 2022; 13:4606-4620. [PMID: 36187248 PMCID: PMC9484426 DOI: 10.1364/boe.468685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 07/28/2022] [Accepted: 08/02/2022] [Indexed: 06/16/2023]
Abstract
Optical-resolution photoacoustic microscopy has been validated as an ideal tool for angiographic studies. Quantitative vascular analysis reveals critical information where vessel segmentation plays the key step. The comm-only used Hessian filter method suffers from varying accuracy due to the multi-kernel strategy. In this work, we developed a Hessian filter-assisted, adaptive thresholding vessel segmentation algorithm. Its performance is validated by a digital phantom and in vivo images which demonstrates a superior and consistent accuracy of 0.987 regardless of kernel selection. Subtle vessel change detection is further tested in two longitudinal studies on blood pressure agents. In the antihypotensive case, the proposed method detected a twice larger vasoconstriction over the Hessian filter method. In the antihypertensive case, the proposed method detected a vasodilation of 21.2%, while the Hessian filter method failed in change detection. The proposed algorithm may further push the limit of quantitative imaging on angiographic applications.
Collapse
Affiliation(s)
- Dong Zhang
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China
- Department of Radiology,
Chinese PLA General Hospital, Beijing
100853, China
| | - Ran Li
- School of Basic Medical Sciences,
North China University of Science and
Technology, Tangshan, Hebei 063210, China
| | - Xin Lou
- Department of Radiology,
Chinese PLA General Hospital, Beijing
100853, China
| | - Jianwen Luo
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China
| |
Collapse
|
16
|
Alvarado-carrillo DE, Cruz-aceves I, Hernández-gonzález MA, López-montero LM. Robust Detection and Modeling of the Major Temporal Arcade in Retinal Fundus Images. Mathematics 2022; 10:1334. [DOI: 10.3390/math10081334] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
The Major Temporal Arcade (MTA) is a critical component of the retinal structure that facilitates clinical diagnosis and monitoring of various ocular pathologies. Although recent works have addressed the quantitative analysis of the MTA through parametric modeling, their efforts are strongly based on an assumption of symmetry in the MTA shape. This work presents a robust method for the detection and piecewise parametric modeling of the MTA in fundus images. The model consists of a piecewise parametric curve with the ability to consider both symmetric and asymmetric scenarios. In an initial stage, multiple models are built from random blood vessel points taken from the blood-vessel segmented retinal image, following a weighted-RANSAC strategy. To choose the final model, the algorithm extracts blood-vessel width and grayscale-intensity features and merges them to obtain a coarse MTA probability function, which is used to weight the percentage of inlier points for each model. This procedure promotes selecting a model based on points with high MTA probability. Experimental results in the public benchmark dataset Digital Retinal Images for Vessel Extraction (DRIVE), for which manual MTA delineations have been prepared, indicate that the proposed method outperforms existing approaches with a balanced Accuracy of 0.7067, Mean Distance to Closest Point of 7.40 pixels, and Hausdorff Distance of 27.96 pixels, while demonstrating competitive results in terms of execution time (9.93 s per image).
Collapse
|
17
|
|
18
|
Więcławek W, Danch-Wierzchowska M, Rudzki M, Sędziak-Marcinek B, Teper SJ. Ultra-Widefield Fluorescein Angiography Image Brightness Compensation Based on Geometrical Features. Sensors (Basel) 2021; 22:12. [PMID: 35009554 PMCID: PMC8747562 DOI: 10.3390/s22010012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 12/08/2021] [Accepted: 12/16/2021] [Indexed: 06/14/2023]
Abstract
Ultra-widefield fluorescein angiography (UWFA) is an emerging imaging modality used to characterise pathologies in the retinal vasculature, such as microaneurysms (MAs) and vascular leakages. Despite its potential value for diagnosis and disease screening, objective quantitative assessment of retinal pathologies by UWFA is currently limited because laborious manual processing is required. In this report, we describe a geometrical method for uneven brightness compensation inherent to UWFA imaging technique. The correction function is based on the geometrical eyeball shape, therefore it is fully automated and depends only on pixel distance from the center of the imaged retina. The method's performance was assessed on a database containing 256 UWFA images with the use of several image quality measures that show the correction method improves image quality. The method is also compared to the commonly used CLAHE approach and was also employed in a pilot study for vascular segmentation, giving a noticeable improvement in segmentation results. Therefore, the method can be used as an image preprocessing step in retinal UWFA image analysis.
Collapse
Affiliation(s)
- Wojciech Więcławek
- Faculty of Biomedical Engineering, Silesian University of Technology, Roosevelta St. 40, 41-800 Zabrze, Poland; (M.D.-W.); (M.R.)
| | - Marta Danch-Wierzchowska
- Faculty of Biomedical Engineering, Silesian University of Technology, Roosevelta St. 40, 41-800 Zabrze, Poland; (M.D.-W.); (M.R.)
| | - Marcin Rudzki
- Faculty of Biomedical Engineering, Silesian University of Technology, Roosevelta St. 40, 41-800 Zabrze, Poland; (M.D.-W.); (M.R.)
| | - Bogumiła Sędziak-Marcinek
- Clinical Department of Ophthalmology, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Panewnicka St. 65, 40-760 Katowice, Poland; (B.S.-M.); (S.J.T.)
| | - Slawomir Jan Teper
- Clinical Department of Ophthalmology, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Panewnicka St. 65, 40-760 Katowice, Poland; (B.S.-M.); (S.J.T.)
| |
Collapse
|
19
|
Ooi AZH, Embong Z, Abd Hamid AI, Zainon R, Wang SL, Ng TF, Hamzah RA, Teoh SS, Ibrahim H. Interactive Blood Vessel Segmentation from Retinal Fundus Image Based on Canny Edge Detector. Sensors (Basel) 2021; 21:s21196380. [PMID: 34640698 PMCID: PMC8512020 DOI: 10.3390/s21196380] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 09/16/2021] [Accepted: 09/21/2021] [Indexed: 11/16/2022]
Abstract
Optometrists, ophthalmologists, orthoptists, and other trained medical professionals use fundus photography to monitor the progression of certain eye conditions or diseases. Segmentation of the vessel tree is an essential process of retinal analysis. In this paper, an interactive blood vessel segmentation from retinal fundus image based on Canny edge detection is proposed. Semi-automated segmentation of specific vessels can be done by simply moving the cursor across a particular vessel. The pre-processing stage includes the green color channel extraction, applying Contrast Limited Adaptive Histogram Equalization (CLAHE), and retinal outline removal. After that, the edge detection techniques, which are based on the Canny algorithm, will be applied. The vessels will be selected interactively on the developed graphical user interface (GUI). The program will draw out the vessel edges. After that, those vessel edges will be segmented to bring focus on its details or detect the abnormal vessel. This proposed approach is useful because different edge detection parameter settings can be applied to the same image to highlight particular vessels for analysis or presentation.
Collapse
Affiliation(s)
- Alexander Ze Hwan Ooi
- School of Electrical & Electronic Engineering, Engineering Campus, Universiti Sains Malaysia, Nibong Tebal 14300, Pulau Pinang, Malaysia; (A.Z.H.O.); (S.S.T.)
| | - Zunaina Embong
- Department of Ophthalmology, School of Medical Sciences, Health Campus, Universiti Sains Malaysia, Kubang Kerian 16150, Kelantan, Malaysia;
| | - Aini Ismafairus Abd Hamid
- Department of Neurosciences, School of Medical Sciences, Health Campus, Universiti Sains Malaysia, Kubang Kerian 16150, Kelantan, Malaysia;
- Brain and Behaviour Cluster, School of Medical Sciences, Health Campus, Universiti Sains Malaysia, Kubang Kerian 16150, Kelantan, Malaysia
| | - Rafidah Zainon
- Oncological and Radiological Sciences Cluster, Advanced Medical and Dental Institute (AMDI), Universiti Sains Malaysia, SAINS@BERTAM, Kepala Batas 13200, Pulau Pinang, Malaysia;
| | - Shir Li Wang
- Faculty of Art, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Tanjong Malim 35900, Perak, Malaysia;
| | - Theam Foo Ng
- Centre of Global Sustainability Studies (CGSS), Level 5, Hamzah Sendut Library, Universiti Sains Malaysia, USM, Minden 11800, Pulau Pinang, Malaysia;
| | - Rostam Affendi Hamzah
- Fakulti Teknologi Kejuruteraan Elektrik dan Elektronik, Universiti Teknikal Malaysia Melaka, Durian Tunggal 76100, Melaka, Malaysia;
| | - Soo Siang Teoh
- School of Electrical & Electronic Engineering, Engineering Campus, Universiti Sains Malaysia, Nibong Tebal 14300, Pulau Pinang, Malaysia; (A.Z.H.O.); (S.S.T.)
| | - Haidi Ibrahim
- School of Electrical & Electronic Engineering, Engineering Campus, Universiti Sains Malaysia, Nibong Tebal 14300, Pulau Pinang, Malaysia; (A.Z.H.O.); (S.S.T.)
- Correspondence:
| |
Collapse
|
20
|
|