Vessel extraction in medical images by wave-propagation and traceback

Semi-Supervised Learning Method of U-Net Deep Learning Network for Blood Vessel Segmentation in Retinal Images

Blood vessel segmentation methods based on deep neural networks have achieved satisfactory results. However, these methods are usually supervised learning methods, which require large numbers of retinal images with high quality pixel-level ground-truth labels. In practice, the task of labeling these retinal images is very costly, financially and in human effort. To deal with these problems, we propose a semi-supervised learning method which can be used in blood vessel segmentation with limited labeled data. In this method, we use the improved U-Net deep learning network to segment the blood vessel tree. On this basis, we implement the U-Net network-based training dataset updating strategy. A large number of experiments are presented to analyze the segmentation performance of the proposed semi-supervised learning method. The experiment results demonstrate that the proposed methodology is able to avoid the problems of insufficient hand-labels, and achieve satisfactory performance.

Coronary angiography video segmentation method for assisting cardiovascular disease interventional treatment

Background

Coronary heart disease is one of the diseases with the highest mortality rate. Due to the important position of cardiovascular disease prevention and diagnosis in the medical field, the segmentation of cardiovascular images has gradually become a research hotspot. How to segment accurate blood vessels from coronary angiography videos to assist doctors in making accurate analysis has become the goal of our research.

Method

Based on the U-net architecture, we use a context-based convolutional network for capturing more information of the vessel in the video. The proposed method includes three modules: the sequence encoder module, the sequence decoder module, and the sequence filter module. The high-level information of the feature is extracted in the encoder module. Multi-kernel pooling layers suitable for the extraction of blood vessels are added before the decoder module. In the filter block, we add a simple temporal filter to reducing inter-frame flickers.

Results

The performance comparison with other method shows that our work can achieve 0.8739 in Sen, 0.9895 in Acc. From the performance of the results, the accuracy of our method is significantly improved. The performance benefit from the algorithm architecture and our enlarged dataset.

Conclusion

Compared with previous methods that only focus on single image analysis, our method can obtain more coronary information through image sequences. In future work, we will extend the network to 3D networks.

Retinal blood vessel segmentation based on Densely Connected U-Net

The segmentation of blood vessels from retinal images is an important and challenging task in medical analysis and diagnosis. This paper proposes a new architecture of the U-Net network for retinal blood vessel segmentation. Adding dense block to U-Net network makes each layer's input come from the all previous layer's output which improves the segmentation accuracy of small blood vessels. The effectiveness of the proposed method has been evaluated on two public datasets (DRIVE and CHASE_DB1). The obtained results (DRIVE： Acc = 0.9559， AUC = 0.9793， CHASE_DB1： Acc = 0.9488， AUC = 0.9785) demonstrate the better performance of the proposed method compared to the state-of-the-art methods. Also, the results show that our method achieves better results for the segmentation of small blood vessels and can be helpful to evaluate related ophthalmic diseases.

Retinal blood vessel extraction employing effective image features and combination of supervised and unsupervised machine learning methods

In medicine, retinal vessel analysis of fundus images is a prominent task for the screening and diagnosis of various ophthalmological and cardiovascular diseases. In this research, a method is proposed for extracting the retinal blood vessels employing a set of effective image features and combination of supervised and unsupervised machine learning techniques. Further to the common features used in extracting blood vessels, three strong features having a significant influence on the accuracy of the vessel extraction are utilized. The selected combination of the different types of individually efficient features results in a rich local information with better discrimination for vessel and non-vessel pixels. The proposed method first extracts the thick and clear vessels in an unsupervised manner, and then, it extracts the thin vessels in a supervised way. The goal of the combination of the supervised and unsupervised methods is to deal with the problem of intra-class high variance of image features calculated from various vessel pixels. The proposed method is evaluated on three publicly available databases DRIVE, STARE and CHASE_DB1. The obtained results (DRIVE: Acc = 0.9531, AUC = 0.9752; STARE: Acc = 0.9691, AUC = 0.9853; CHASE_DB1: Acc = 0.9623, AUC = 0.9789) demonstrate the better performance of the proposed method compared to the state-of-the-art methods.

A Hierarchical Image Matting Model for Blood Vessel Segmentation in Fundus Images

In this paper, a hierarchical image matting model is proposed to extract blood vessels from fundus images. More specifically, a hierarchical strategy is integrated into the image matting model for blood vessel segmentation. Normally the matting models require a user specified trimap, which separates the input image into three regions: the foreground, background and unknown regions. However, creating a user specified trimap is laborious for vessel segmentation tasks. In this paper, we propose a method that first generates trimap automatically by utilizing region features of blood vessels, then applies a hierarchical image matting model to extract the vessel pixels from the unknown regions. The proposed method has low calculation time and outperforms many other state-of-art supervised and unsupervised methods. It achieves a vessel segmentation accuracy of 96.0%, 95.7% and 95.1% in an average time of 10.72s, 15.74s and 50.71s on images from three publicly available fundus image datasets DRIVE, STARE, and CHASE DB1, respectively.

Retinal Blood Vessel Segmentation Using Extreme Learning Machine

Extreme learning machine (ELM) is an effective machine learning technique that widely used in image processing. In this paper, a new supervised method for segmenting blood vessels in retinal images is proposed based on the ELM classifier. The proposed algorithm first constructs a 7-D feature vector using multi-scale Gabor filter, Hessian matrix and bottom-hat transformation. Then, an ELM classifier is trained on gold standard examples of vessel segmentation images to classify previous unseen images. The algorithm was tested on the publicly available DRIVE database – a digital image database for vessel extraction. Experimental results on both real-captured images and public database images demonstrate that our method shows comparative performance against other methods, which make the proposed algorithm a suitable tool for automated retinal image analysis.

MRI-Based Medial Axis Extraction and Boundary Segmentation of Cranial Nerves Through Discrete Deformable 3D Contour and Surface Models

This paper presents a segmentation technique to identify the medial axis and the boundary of cranial nerves. We utilize a 3-D deformable one-simplex discrete contour model to extract the medial axis of each cranial nerve. This contour model represents a collection of two-connected vertices linked by edges, where vertex position is determined by a Newtonian expression for vertex kinematics featuring internal and external forces, the latter of which include attractive forces toward the nerve medial axis. We exploit multiscale vesselness filtering and minimal path techniques in the medial axis extraction method, which also computes a radius estimate along the path. Once we have the medial axis and the radius function of a nerve, we identify the nerve surface using a two-simplex deformable model, which expands radially and can accommodate any nerve shape. As a result, the method proposed here combines the benefits of explicit contour and surface models, while also achieving a cornerstone for future work that will emphasize shape statistics, static collision with other critical structures, and tree-shape analysis.

Automatic Retinal Image Registration Using Blood Vessel Segmentation and SIFT Feature

Automatic retinal image registration is still a great challenge in computer aided diagnosis and screening system. In this paper, a new retinal image registration method is proposed based on the combination of blood vessel segmentation and scale invariant feature transform (SIFT) feature. The algorithm includes two stages: retinal image segmentation and registration. In the segmentation stage, the blood vessel is segmented by using the guided filter to enhance the vessel structure and the bottom-hat transformation to extract blood vessel. In the registration stage, the SIFT algorithm is adopted to detect the feature of vessel segmentation image, complemented by using a random sample consensus (RANSAC) algorithm to eliminate incorrect matches. We evaluate our method from both segmentation and registration aspects. For segmentation evaluation, we test our method on DRIVE database, which provides manually labeled images from two specialists. The experimental results show that our method achieves 0.9562 in accuracy (Acc), which presents competitive performance compare to other existing segmentation methods. For registration evaluation, we test our method on STARE database, and the experimental results demonstrate the superior performance of the proposed method, which makes the algorithm a suitable tool for automated retinal image analysis.

The adaptive computer-aided diagnosis system based on tumor sizes for the classification of breast tumors detected at screening ultrasound

Screening ultrasound (US) is increasingly used as a supplement to mammography in women with dense breasts, and more than 80% of cancers detected by US alone are 1cm or smaller. An adaptive computer-aided diagnosis (CAD) system based on tumor size was proposed to classify breast tumors detected at screening US images using quantitative morphological and textural features. In the present study, a database containing 156 tumors (78 benign and 78 malignant) was separated into two subsets of different tumor sizes (<1cm and ⩾1cm) to explore the improvement in the performance of the CAD system. After adaptation, the accuracies, sensitivities, specificities and Az values of the CAD for the entire database increased from 73.1% (114/156), 73.1% (57/78), 73.1% (57/78), and 0.790 to 81.4% (127/156), 83.3% (65/78), 79.5% (62/78), and 0.852, respectively. In the data subset of tumors larger than 1cm, the performance improved from 66.2% (51/77), 68.3% (28/41), 63.9% (23/36), and 0.703 to 81.8% (63/77), 85.4% (35/41), 77.8% (28/36), and 0.855, respectively. The proposed CAD system can be helpful to classify breast tumors detected at screening US.

Vascular segmentation of head phase-contrast magnetic resonance angiograms using grayscale and shape features

BACKGROUND AND OBJECTIVE

In neurosurgery planning, vascular structures must be predetermined, which can guarantee the security of the operation carried out in the case of avoiding blood vessels. In this paper, an automatic algorithm of vascular segmentation, which combined the grayscale and shape features of the blood vessels, is proposed to extract 3D vascular structures from head phase-contrast magnetic resonance angiography dataset.

METHODS

First, a cost function of mis-segmentation is introduced on the basis of traditional Bayesian statistical classification, and the blood vessel of weak grayscale that tended to be misclassified into background will be preserved. Second, enhanced vesselness image is obtained according to the shape-based multiscale vascular enhancement filter. Third, a new reconstructed vascular image is established according to the fusion of vascular grayscale and shape features using Dempster-Shafer evidence theory; subsequently, the corresponding segmentation structures are obtained. Finally, according to the noise distribution characteristic of the data, segmentation ratio coefficient, which increased linearly from top to bottom, is proposed to control the segmentation result, thereby preventing over-segmentation.

RESULTS

Experiment results show that, through the proposed method, vascular structures can be detected not only when both grayscale and shape features are strong, but also when either of them is strong. Compared with traditional grayscale feature- and shape feature-based methods, it is better in the evaluation of testing in segmentation accuracy, and over-segmentation and under-segmentation ratios.

CONCLUSIONS

The proposed grayscale and shape features combined vascular segmentation is not only effective but also accurate. It may be used for diagnosis of vascular diseases and planning of neurosurgery.

Interactive retinal blood flow analysis of the macular region

The study of retinal hemodynamics plays an important role to understand the onset and progression of diabetic retinopathy. In this work, we developed an interactive retinal analysis tool to quantitatively measure the blood flow velocity (BFV) and blood flow rate (BFR) in the macular region using the Retinal Function Imager (RFI). By employing a high definition stroboscopic fundus camera, the RFI device is able to assess retinal blood flow characteristics in vivo. However, the measurements of BFV using a user-guided vessel segmentation tool may induce significant inter-observer differences and BFR is not provided in the built-in software. In this work, we have developed an interactive tool to assess the retinal BFV and BFR in the macular region. Optical coherence tomography data was registered with the RFI image to locate the fovea accurately. The boundaries of the vessels were delineated on a motion contrast enhanced image and BFV was computed by maximizing the cross-correlation of pixel intensities in a ratio video. Furthermore, we were able to calculate the BFR in absolute values (μl/s). Experiments were conducted on 122 vessels from 5 healthy and 5 mild non-proliferative diabetic retinopathy (NPDR) subjects. The Pearson's correlation of the vessel diameter measurements between our method and manual labeling on 40 vessels was 0.984. The intraclass correlation (ICC) of BFV between our proposed method and built-in software was 0.924 and 0.830 for vessels from healthy and NPDR subjects, respectively. The coefficient of variation between repeated sessions was reduced significantly from 22.5% to 15.9% in our proposed method (p<0.001).

A framework for retinal vasculature segmentation based on matched filters

Background

Automatic fundus image processing plays a significant role in computer-assisted retinopathy diagnosis. As retinal vasculature is an important anatomical structure in ophthalmic images, recently, retinal vasculature segmentation has received considerable attention from researchers. A segmentation method usually consists of three steps: preprocessing, segmentation, post-processing. Most of the existing methods emphasize on the segmentation step. In our opinion, the vessels and background can be easily separable when suitable preprocessing exists.

Methods

This paper represents a new matched filter-based vasculature segmentation method for 2-D retinal images. First of all, a raw segmentation is acquired by thresholding the images preprocessed using weighted improved circular gabor filter and multi-directional multi-scale second derivation of Gaussian. After that, the raw segmented image is fine-tuned by a set of novel elongating filters. Finally, we eliminate the speckle like regions and isolated pixels, most of which are non-vessel noises and miss-classified fovea or pathological regions.

Results

The performance of the proposed method is examined on two popularly used benchmark databases: DRIVE and STARE. The accuracy values are 95.29 and 95.69 %, respectively, without a significant degradation of specificity and sensitivity.

Conclusion

The performance of the proposed method is significantly better than almost all unsupervised methods, in addition, comparable to most of the existing supervised vasculature segmentation methods.

Automated identification of retinal vessels using a multiscale directional contrast quantification (MDCQ) strategy

PURPOSE

A novel algorithm is presented to automatically identify the retinal vessels depicted in color fundus photographs.

METHODS

The proposed algorithm quantifies the contrast of each pixel in retinal images at multiple scales and fuses the resulting consequent contrast images in a progressive manner by leveraging their spatial difference and continuity. The multiscale strategy is to deal with the variety of retinal vessels in width, intensity, resolution, and orientation; and the progressive fusion is to combine consequent images and meanwhile avoid a sudden fusion of image noise and/or artifacts in space. To quantitatively assess the performance of the algorithm, we tested it on three publicly available databases, namely, DRIVE, STARE, and HRF. The agreement between the computer results and the manual delineation in these databases were quantified by computing their overlapping in both area and length (centerline). The measures include sensitivity, specificity, and accuracy.

RESULTS

For the DRIVE database, the sensitivities in identifying vessels in area and length were around 90% and 70%, respectively, the accuracy in pixel classification was around 99%, and the precisions in terms of both area and length were around 94%. For the STARE database, the sensitivities in identifying vessels were around 90% in area and 70% in length, and the accuracy in pixel classification was around 97%. For the HRF database, the sensitivities in identifying vessels were around 92% in area and 83% in length for the healthy subgroup, around 92% in area and 75% in length for the glaucomatous subgroup, around 91% in area and 73% in length for the diabetic retinopathy subgroup. For all three subgroups, the accuracy was around 98%.

CONCLUSIONS

The experimental results demonstrate that the developed algorithm is capable of identifying retinal vessels depicted in color fundus photographs in a relatively reliable manner.

Calibration-free coronary artery measurements for interventional device sizing using inverse geometry x-ray fluoroscopy: in vivo validation

Proper sizing of interventional devices to match coronary vessel dimensions improves procedural efficiency and therapeutic outcomes. We have developed a method that uses an inverse geometry x-ray fluoroscopy system [scanning beam digital x-ray (SBDX)] to automatically determine vessel dimensions from angiograms without the need for magnification calibration or optimal views. For each frame period (1/15th of a second), SBDX acquires a sequence of narrow beam projections and performs digital tomosynthesis at multiple plane positions. A three-dimensional model of the vessel is reconstructed by localizing the depth of the vessel edges from the tomosynthesis images, and the model is used to calculate the length and diameter in units of millimeters. The in vivo algorithm performance was evaluated in a healthy porcine model by comparing end-diastolic length and diameter measurements from SBDX to coronary computed tomography angiography (CCTA) and intravascular ultrasound (IVUS), respectively. The length error was -0.49 ± 1.76 mm(SBDX- CCTA, mean ± 1 SD). The diameter error was 0.07 ± 0.27 mm (SBDX - minimum IVUS diameter, mean ± 1 SD). The in vivo agreement between SBDX-based vessel sizing and gold standard techniques supports the feasibility of calibration-free coronary vessel sizing using inverse geometry x-ray fluoroscopy.

Calibration-Free Coronary Artery Measurements for Interventional Device Sizing using Inverse Geometry X-ray Fluoroscopy: In Vivo Validation

Proper sizing of interventional devices to match coronary vessel dimensions improves procedural efficiency and therapeutic outcomes. We have developed a novel method using inverse geometry x-ray fluoroscopy to automatically determine vessel dimensions without the need for magnification calibration or optimal views. To validate this method in vivo, we compared results to intravascular ultrasound (IVUS) and coronary computed tomography angiography (CCTA) in a healthy porcine model. Coronary angiography was performed using Scanning-Beam Digital X-ray (SBDX), an inverse geometry fluoroscopy system that performs multiplane digital x-ray tomosynthesis in real time. From a single frame, 3D reconstruction of the arteries was performed by localizing the depth of vessel lumen edges. The 3D model was used to directly calculate length and to determine the best imaging plane to use for diameter measurements, where out-of-plane blur was minimized and the known pixel spacing was used to obtain absolute vessel diameter. End-diastolic length and diameter measurements were compared to measurements from CCTA and IVUS, respectively. For vessel segment lengths measuring 6 mm to 73 mm by CCTA, the SBDX length error was -0.49 ± 1.76 mm (SBDX - CCTA, mean ± 1 SD). For vessel diameters measuring 2.1 mm to 3.6 mm by IVUS, the SBDX diameter error was 0.07 ± 0.27 mm (SBDX - minimum IVUS diameter, mean ± 1 SD). The in vivo agreement between SBDX-based vessel sizing and gold standard techniques supports the feasibility of calibration-free coronary vessel sizing using inverse geometry x-ray fluoroscopy.

Robust vessel segmentation in fundus images

One of the most common modalities to examine the human eye is the eye-fundus photograph. The evaluation of fundus photographs is carried out by medical experts during time-consuming visual inspection. Our aim is to accelerate this process using computer aided diagnosis. As a first step, it is necessary to segment structures in the images for tissue differentiation. As the eye is the only organ, where the vasculature can be imaged in an in vivo and noninterventional way without using expensive scanners, the vessel tree is one of the most interesting and important structures to analyze. The quality and resolution of fundus images are rapidly increasing. Thus, segmentation methods need to be adapted to the new challenges of high resolutions. In this paper, we present a method to reduce calculation time, achieve high accuracy, and increase sensitivity compared to the original Frangi method. This method contains approaches to avoid potential problems like specular reflexes of thick vessels. The proposed method is evaluated using the STARE and DRIVE databases and we propose a new high resolution fundus database to compare it to the state-of-the-art algorithms. The results show an average accuracy above 94% and low computational needs. This outperforms state-of-the-art methods.

An approach to localize the retinal blood vessels using bit planes and centerline detection

The change in morphology, diameter, branching pattern or tortuosity of retinal blood vessels is an important indicator of various clinical disorders of the eye and the body. This paper reports an automated method for segmentation of blood vessels in retinal images. A unique combination of techniques for vessel centerlines detection and morphological bit plane slicing is presented to extract the blood vessel tree from the retinal images. The centerlines are extracted by using the first order derivative of a Gaussian filter in four orientations and then evaluation of derivative signs and average derivative values is performed. Mathematical morphology has emerged as a proficient technique for quantifying the blood vessels in the retina. The shape and orientation map of blood vessels is obtained by applying a multidirectional morphological top-hat operator with a linear structuring element followed by bit plane slicing of the vessel enhanced grayscale image. The centerlines are combined with these maps to obtain the segmented vessel tree. The methodology is tested on three publicly available databases DRIVE, STARE and MESSIDOR. The results demonstrate that the performance of the proposed algorithm is comparable with state of the art techniques in terms of accuracy, sensitivity and specificity.

Blood vessel segmentation methodologies in retinal images--a survey

Retinal vessel segmentation algorithms are a fundamental component of automatic retinal disease screening systems. This work examines the blood vessel segmentation methodologies in two dimensional retinal images acquired from a fundus camera and a survey of techniques is presented. The aim of this paper is to review, analyze and categorize the retinal vessel extraction algorithms, techniques and methodologies, giving a brief description, highlighting the key points and the performance measures. We intend to give the reader a framework for the existing research; to introduce the range of retinal vessel segmentation algorithms; to discuss the current trends and future directions and summarize the open problems. The performance of algorithms is compared and analyzed on two publicly available databases (DRIVE and STARE) of retinal images using a number of measures which include accuracy, true positive rate, false positive rate, sensitivity, specificity and area under receiver operating characteristic (ROC) curve.

Automated quantification of carotid artery stenosis on contrast-enhanced MRA data using a deformable vascular tube model

The purpose of this study was to develop and validate a method for automated segmentation of the carotid artery lumen from volumetric MR Angiographic (MRA) images using a deformable tubular 3D Non-Uniform Rational B-Splines (NURBS) model. A flexible 3D tubular NURBS model was designed to delineate the carotid arterial lumen. User interaction was allowed to guide the model by placement of forbidden areas. Contrast-enhanced MRA (CE-MRA) from 21 patients with carotid atherosclerotic disease were included in this study. The validation was performed against expert drawn contours on multi-planar reformatted image slices perpendicular to the artery. Excellent linear correlations were found on cross-sectional area measurement (r = 0.98, P < 0.05) and on luminal diameter (r = 0.98, P < 0.05). Strong match in terms of the Dice similarity indices were achieved: 0.95 ± 0.02 (common carotid artery), 0.90 ± 0.07 (internal carotid artery), 0.87 ± 0.07 (external carotid artery), 0.88 ± 0.09 (carotid bifurcation) and 0.75 ± 0.20 (stenosed segments). Slight overestimation of stenosis grading by the automated method was observed. The mean differences was 7.20% (SD = 21.00%) and 5.2% (SD = 21.96%) when validated against two observers. Reproducibility in stenosis grade calculation by the automated method was high; the mean difference between two repeated analyses was 1.9 ± 7.3%. In conclusion, the automated method shows high potential for clinical application in the analysis of CE-MRA of carotid arteries.

Automatic segmentation of the wire frame of stent grafts from CT data

Endovascular aortic replacement (EVAR) is an established technique, which uses stent grafts to treat aortic aneurysms in patients at risk of aneurysm rupture. Late stent graft failure is a serious complication in endovascular repair of aortic aneurysms. Better understanding of the motion characteristics of stent grafts will be beneficial for designing future devices. In addition, analysis of stent graft movement in individual patients in vivo can be valuable for predicting stent graft failure in these patients. To be able to gather information on stent graft motion in a quick and robust fashion, we propose an automatic method to segment stent grafts from CT data, consisting of three steps: the detection of seed points, finding the connections between these points to produce a graph, and graph processing to obtain the final geometric model in the form of an undirected graph. Using annotated reference data, the method was optimized and its accuracy was evaluated. The experiments were performed using data containing the AneuRx and Zenith stent grafts. The algorithm is robust for noise and small variations in the used parameter values, does not require much memory according to modern standards, and is fast enough to be used in a clinical setting (65 and 30s for the two stent types, respectively). Further, it is shown that the resulting graphs have a 95% (AneuRx) and 92% (Zenith) correspondence with the annotated data. The geometric model produced by the algorithm allows incorporation of high level information and material properties. This enables us to study the in vivo motions and forces that act on the frame of the stent. We believe that such studies will provide new insights into the behavior of the stent graft in vivo, enables the detection and prediction of stent failure in individual patients, and can help in designing better stent grafts in the future.

Anisotropic path searching for automatic neuron reconstruction

Full reconstruction of neuron morphology is of fundamental interest for the analysis and understanding of their functioning. We have developed a novel method capable of automatically tracing neurons in three-dimensional microscopy data. In contrast to template-based methods, the proposed approach makes no assumptions about the shape or appearance of neurite structure. Instead, an efficient seeding approach is applied to capture complex neuronal structures and the tracing problem is solved by computing the optimal reconstruction with a weighted graph. The optimality is determined by the cost function designed for the path between each pair of seeds and by topological constraints defining the component interrelations and completeness. In addition, an automated neuron comparison method is introduced for performance evaluation and structure analysis. The proposed algorithm is computationally efficient and has been validated using different types of microscopy data sets including Drosophila's projection neurons and fly neurons with presynaptic sites. In all cases, the approach yielded promising results.

Automatic selection of representative slice from cine-loops of real-time sonoelastography for classifying solid breast masses

This study aimed to evaluate the performance of automatic selection of representative slice from cine-loops of real-time sonoelastography for classifying benign and malignant breast masses. This retrospective study included 141 ultrasound elastographic studies (93 benign and 48 malignant masses). A novel computer-assisted system was developed for the automatic segmentation of the targeted lesion from cine-loops of real-time sonoelastography. Its hard ratio, defined as the ratio of the number of hard pixels within the tumor divided by the total number of pixels of the whole tumor, was also calculated. The targeted mass was segmented by edge-detection and region growing methods, with combined motion registration after manually defining the original seed. Signal-to-noise ratio (SNR(e)) and contrast-to-noise ratio (CNR(e)) of ultrasound elastogram were computed to obtain an optimum slice for differentiating benign and malignant lesions. The diagnostic results of automatic slice selection using maximum strain, maximum SNR(e), maximum CNR(e), maximum compression and the slices selected by radiologists were compared. Mann-Whitney U test, performance indexes and receiver operating characteristic (ROC) curves were used for statistical analysis. Performance using the maximum SNR(e) (accuracy 84.4%, sensitivity 83.3%, specificity 85.0% and A(z) value 0.90) was the best as compared with those of maximum CNR(e) (82.3%, 79.2%, 83.9% and 0.88, respectively), maximum compression (78.0%, 79.2%, 77.4% and 0.85, respectively), maximum strain (79.4%, 79.2%, 79.6% and 0.87, respectively) and radiologists' selection (77.3%, 77.1%, 77.4% and 0.80, respectively). Automatic selection of representative slice from the cine-loops of real-time sonoelastography is a practical, objective and accurate approach for classifying solid breast masses.

Breast tumor classification using fuzzy clustering for breast elastography

Elastography is a new ultrasound imaging technique to provide the information about relative tissue stiffness. The elasticity information provided by this dynamic imaging method has proven to be helpful in distinguishing benign and malignant breast tumors. In previous studies for computer-aided diagnosis (CAD), the tumor contour was manually segmented and each pixel in the elastogram was classified into hard or soft tissue using the simple thresholding technique. In this paper, the tumor contour was automatically segmented by the level set method to provide more objective and reliable tumor contour for CAD. Moreover, the elasticity of each pixel inside each tumor was classified by the fuzzy c-means clustering technique to obtain a more precise diagnostic result. The test elastography database included 66 benign and 31 malignant biopsy-proven tumors. In the experiments, the accuracy, sensitivity, specificity and the area index Az under the receiver operating characteristic curve for the classification of solid breast masses were 83.5% (81/97), 83.9% (26/31), 83.3% (55/66) and 0.902 for the fuzzy c-means clustering method, respectively, and 59.8% (58/97), 96.8% (30/31), 42.4% (28/66) and 0.818 for the conventional thresholding method, respectively. The differences of accuracy, specificity and Az value were statistically significant (p < 0.05). We conclude that the proposed method has the potential to provide a CAD tool to help physicians to more reliably and objectively diagnose breast tumors using elastography.

A model-based consecutive scanline tracking method for extracting vascular networks from 2-D digital subtraction angiograms

We propose a new model-based algorithm for the automated tracking of vascular networks in 2-D digital subtraction angiograms. Consecutive scanline profiles are fitted by a parametric imaging model to estimate local vessel center point, radius, edge locations and direction. An adaptive tracking strategy is applied with appropriate termination criteria to track each vessel segment. When tracking stops, to prevent premature termination and to detect bifurcations, a look ahead detection scheme is used to search for possible continuation points of the same vessel segment or those of its bifurcated segments. The proposed algorithm can automatically extract the majority of the vascular network without human interaction other than initializing the start point and direction. Compared to other tracking methods, the proposed method highlights accurate estimation of local vessel geometry. Accurate geometric information and a hierarchical vessel network are obtained which can be used for further quantitative analysis of arterial networks to obtain flow conductance estimates.

A comprehensive approach to the analysis of contrast enhanced cardiac MR images

Current magnetic resonance imaging (MRI) technology allows the determination of patient-individual coronary tree structure, detection of infarctions, and assessment of myocardial perfusion. Joint inspection of these three aspects yields valuable information for therapy planning, e.g., through classification of myocardium into healthy tissue, regions showing a reversible hypoperfusion, and infarction with additional information on the corresponding supplying artery. Standard imaging protocols normally provide image data with different orientations, resolutions and coverages for each of the three aspects, which makes a direct comparison of analysis results difficult. The purpose of this work is to develop methods for the alignment and combined analysis of these images. The proposed approach is applied to 21 datasets of healthy and diseased patients from the clinical routine. The evaluation shows that, despite limitations due to typical MRI artifacts, combined inspection is feasible and can yield clinically useful information.

Gap filling of 3-D microvascular networks by tensor voting

We present a new algorithm which merges discontinuities in 3-D images of tubular structures presenting undesirable gaps. The application of the proposed method is mainly associated to large 3-D images of microvascular networks. In order to recover the real network topology, we need to fill the gaps between the closest discontinuous vessels. The algorithm presented in this paper aims at achieving this goal. This algorithm is based on the skeletonization of the segmented network followed by a tensor voting method. It permits to merge the most common kinds of discontinuities found in microvascular networks. It is robust, easy to use, and relatively fast. The microvascular network images were obtained using synchrotron tomography imaging at the European Synchrotron Radiation Facility. These images exhibit samples of intracortical networks. Representative results are illustrated.

3D planar reformation of vascular central axis surface with biconvex slab

Curved multi-planar reformation (curved MPR) is one of the commonly used vascular visualization methods in clinics. It re-samples and visualizes the vascular central axis surface (VCAS), which is a curved surface passing through the vascular central axis (VCA) or vessel centerline. The rotation of the VCAS along the VCA generates a set of 2D images. In this paper, we introduce a 3D curved MPR method, VCAS planar reformation (VPR) by a convex hull, called a biconvex slab. The entire vessel is enclosed within a biconvex slab and rendered in one image by volume rendering, such as MIP or X-ray. The method is applied to computed tomographic angiography (CTA) data sets. The resulting image is clear and free from obstruction by bones and other adjacent organs.

Computerized analysis of digital subtraction angiography: a tool for quantitative in-vivo vascular imaging

The purpose of our study was to develop a user-independent computerized tool for the automated segmentation and quantitative assessment of in vivo-acquired digital subtraction angiography (DSA) images. Vessel enhancement was accomplished based on the concept of image structural tensor. The developed software was tested on a series of DSA images acquired from one animal and two human angiogenesis models. Its performance was evaluated against manually segmented images. A receiver's operating characteristic curve was obtained for every image with regard to the different percentages of the image histogram. The area under the mean curve was 0.89 for the experimental angiogenesis model and 0.76 and 0.86 for the two clinical angiogenesis models. The coordinates of the operating point were 8.3% false positive rate and 92.8% true positive rate for the experimental model. Correspondingly for clinical angiogenesis models, the coordinates were 8.6% false positive rate and 89.2% true positive rate and 9.8% false positive rate and 93.8% true positive rate, respectively. A new user-friendly tool for the analysis of vascular networks in DSA images was developed that can be easily used in either experimental or clinical studies. Its main characteristics are robustness and fast and automatic execution.

Segmentation and quantification of human vessels using a 3-D cylindrical intensity model

We introduce a new approach for 3-D segmentation and quantification of vessels. The approach is based on a 3-D cylindrical parametric intensity model, which is directly fitted to the image intensities through an incremental process based on a Kalman filter. Segmentation results are the vessel centerline and shape, i.e., we estimate the local vessel radius, the 3-D position and 3-D orientation, the contrast, as well as the fitting error. We carried out an extensive validation using 3-D synthetic images and also compared the new approach with an approach based on a Gaussian model. In addition, the new model has been successfully applied to segment vessels from 3-D MRA and computed tomography angiography image data. In particular, we compared our approach with an approach based on the randomized Hough transform. Moreover, a validation of the segmentation results based on ground truth provided by a radiologist confirms the accuracy of the new approach. Our experiments show that the new model yields superior results in estimating the vessel radius compared to previous approaches based on a Gaussian model as well as the Hough transform.

System for the analysis and visualization of large 3D anatomical trees

Modern micro-CT and multi-detector helical CT scanners can produce high-resolution 3D digital images of various anatomical trees. The large size and complexity of these trees make it essentially impossible to define them interactively. Automatic approaches have been proposed for a few specific problems, but none of these approaches guarantee extracting geometrically accurate multi-generational tree structures. This paper proposes an interactive system for defining and visualizing large anatomical trees and for subsequent quantitative data mining. The system consists of a large number of tools for automatic image analysis, semi-automatic and interactive tree editing, and an assortment of visualization tools. Results are presented for a variety of 3D high-resolution images.

Analysis of retinal vasculature using a multiresolution Hermite model

This paper presents a vascular representation and segmentation algorithm based on a multiresolution Hermite model (MHM). A two-dimensional Hermite function intensity model is developed which models blood vessel profiles in a quad-tree structure over a range of spatial resolutions. The use of a multiresolution representation simplifies the image modeling and allows for a robust analysis by combining information across scales. Estimation over scale also reduces the overall computational complexity. As well as using MHM for vessel labelling, the local image modeling can accurately represent vessel directions, widths, amplitudes, and branch points which readily enable the global topology to be inferred. An expectation-maximization (EM) type of optimization scheme is used to estimate local model parameters and an information theoretic test is then applied to select the most appropriate scale/feature model for each region of the image. In the final stage, Bayesian stochastic inference is employed for linking the local features to obtain a description of the global vascular structure. After a detailed description and analysis of MHM, experimental results on two standard retinal databases are given that demonstrate its comparative performance. These show MHM to perform comparably with other retinal vessel labelling methods.

Retinal vessel centerline extraction using multiscale matched filters, confidence and edge measures

Motivated by the goals of improving detection of low-contrast and narrow vessels and eliminating false detections at nonvascular structures, a new technique is presented for extracting vessels in retinal images. The core of the technique is a new likelihood ratio test that combines matched-filter responses, confidence measures and vessel boundary measures. Matched filter responses are derived in scale-space to extract vessels of widely varying widths. A vessel confidence measure is defined as a projection of a vector formed from a normalized pixel neighborhood onto a normalized ideal vessel profile. Vessel boundary measures and associated confidences are computed at potential vessel boundaries. Combined, these responses form a six-dimensional measurement vector at each pixel. A training technique is used to develop a mapping of this vector to a likelihood ratio that measures the "vesselness" at each pixel. Results comparing this vesselness measure to matched filters alone and to measures based on the Hessian of intensities show substantial improvements, both qualitatively and quantitatively. The Hessian can be used in place of the matched filter to obtain similar but less-substantial improvements or to steer the matched filter by preselecting kernel orientations. Finally, the new vesselness likelihood ratio is embedded into a vessel tracing framework, resulting in an efficient and effective vessel centerline extraction algorithm.

Level set based cerebral vasculature segmentation and diameter quantification in CT angiography

A level set based method is presented for cerebral vascular tree segmentation from computed tomography angiography (CTA) data. The method starts with bone masking by registering a contrast enhanced scan with a low-dose mask scan in which the bone has been segmented. Then an estimate of the background and vessel intensity distributions is made based on the intensity histogram which is used to steer the level set to capture the vessel boundaries. The relevant parameters of the level set evolution are optimized using a training set. The method is validated by a diameter quantification study which is carried out on phantom data, representing ground truth, and 10 patient data sets. The results are compared to manually obtained measurements by two expert observers. In the phantom study, the method achieves similar accuracy as the observers, but is unbiased whereas the observers are biased, i.e., the results are 0.00+/-0.23 vs. -0.32+/-0.23 mm. Also, the method's reproducibility is slightly better than the inter-and intra-observer variability. In the patient study, the method is in agreement with the observers and also, the method's reproducibility -0.04+/-0.17 mm is similar to the inter-observer variability 0.06+/-0.17 mm. Since the method achieves comparable accuracy and reproducibility as the observers, and since the method achieves better performance than the observers with respect to ground truth, we conclude that the level set based vessel segmentation is a promising method for automated and accurate CTA diameter quantification.

Evaluation of an improved technique for automated center lumen line definition in cardiovascular image data

The aim of the study was to evaluate a new method for automated definition of a center lumen line in vessels in cardiovascular image data. This method, called VAMPIRE, is based on improved detection of vessel-like structures. A multiobserver evaluation study was conducted involving 40 tracings in clinical CTA data of carotid arteries to compare VAMPIRE with an established technique. This comparison showed that VAMPIRE yields considerably more successful tracings and improved handling of stenosis, calcifications, multiple vessels, and nearby bone structures. We conclude that VAMPIRE is highly suitable for automated definition of center lumen lines in vessels in cardiovascular image data.

Automatic selection of parameters for vessel/neurite segmentation algorithms

An automated method is presented for selecting optimal parameter settings for vessel/neurite segmentation algorithms using the minimum description length principle and a recursive random search algorithm. It trades off a probabilistic measure of image-content coverage against its conciseness. It enables nonexpert users to select parameter settings objectively, without knowledge of underlying algorithms, broadening the applicability of the segmentation algorithm, and delivering higher morphometric accuracy. It enables adaptation of parameters across batches of images. It simplifies the user interface to just one optional parameter and reduces the cost of technical support. Finally, the method is modular, extensible, and amenable to parallel computation. The method is applied to 223 images of human retinas and cultured neurons, from four different sources, using a single segmentation algorithm with eight parameters. Improvements in segmentation quality compared to default settings using 1000 iterations ranged from 4.7%-21%. Paired t-tests showed that improvements are statistically significant (p < 0.0005). Most of the improvement occurred in the first 44 iterations. Improvements in description lengths and agreement with the ground truth were strongly correlated (p = 0.78).