Alauddin Bhuiyan

An effective retinal blood vessel segmentation method using multi-scale line detection

Changes in retinal blood vessel features are precursors of serious diseases such as cardiovascular disease and stroke. Therefore, analysis of retinal vascular features can assist in detecting these changes and allow the patient to take action while the disease is still in its early stages. Automation of this process would help to reduce the cost associated with trained graders and remove the issue of inconsistency introduced by manual grading. Among different retinal analysis tasks, retinal blood vessel extraction plays an extremely important role as it is the first essential step before any measurement can be made. In this paper, we present an effective method for automatically extracting blood vessels from colour retinal images. The proposed method is based on the fact that by changing the length of a basic line detector, line detectors at varying scales are achieved. To maintain the strength and eliminate the drawbacks of each individual line detector, the line responses at varying scales are linearly combined to produce the final segmentation for each retinal image. The performance of the proposed method was evaluated both quantitatively and qualitatively on three publicly available DRIVE, STARE, and REVIEW datasets. On DRIVE and STARE datasets, the proposed method achieves high local accuracy (a measure to assess the accuracy at regions around the vessels) while retaining comparable accuracy compared to other existing methods. Visual inspection on the segmentation results shows that the proposed method produces accurate segmentation on central reflex vessels while keeping close vessels well separated. On REVIEW dataset, the vessel width measurements obtained using the segmentations produced by the proposed method are highly accurate and close to the measurements provided by the experts. This has demonstrated the high segmentation accuracy of the proposed method and its applicability for automatic vascular calibre measurement. Other advantages of the proposed method include its efficiency with fast segmentation time, its simplicity and scalability to deal with high resolution retinal images.

Progress on retinal image analysis for age related macular degeneration

Age-related macular degeneration (AMD) is the leading cause of vision loss in those over the age of 50 years in the developed countries. The number is expected to increase by ∼1.5 fold over the next ten years due to an increase in aging population. One of the main measures of AMD severity is the analysis of drusen, pigmentary abnormalities, geographic atrophy (GA) and choroidal neovascularization (CNV) from imaging based on color fundus photograph, optical coherence tomography (OCT) and other imaging modalities. Each of these imaging modalities has strengths and weaknesses for extracting individual AMD pathology and different imaging techniques are used in combination for capturing and/or quantification of different pathologies. Current dry AMD treatments cannot cure or reverse vision loss. However, the Age-Related Eye Disease Study (AREDS) showed that specific anti-oxidant vitamin supplementation reduces the risk of progression from intermediate stages (defined as the presence of either many medium-sized drusen or one or more large drusen) to late AMD which allows for preventative strategies in properly identified patients. Thus identification of people with early stage AMD is important to design and implement preventative strategies for late AMD, and determine their cost-effectiveness. A mass screening facility with teleophthalmology or telemedicine in combination with computer-aided analysis for large rural-based communities may identify more individuals suitable for early stage AMD prevention. In this review, we discuss different imaging modalities that are currently being considered or used for screening AMD. In addition, we look into various automated and semi-automated computer-aided grading systems and related retinal image analysis techniques for drusen, geographic atrophy and choroidal neovascularization detection and/or quantification for measurement of AMD severity using these imaging modalities. We also review the existing telemedicine studies which include diagnosis and management of AMD, and how automated disease grading could benefit telemedicine. As there is no treatment for dry AMD and only early intervention can prevent the late AMD, we emphasize mass screening through a telemedicine platform to enable early detection of AMD. We also provide a comparative study between the imaging modalities and identify potential study areas for further improvement and future research direction in automated AMD grading and screening.

Automatic Detection of Vascular Bifurcations and Crossovers from Color Retinal Fundus Images

Identifying the vascular bifurcations and crossovers in the retinal image is helpful for predicting many cardiovascular diseases and can be used as biometric features and for image registration. In this paper, we propose an efficient method to detect vascular bifurcations and crossovers based on the vessel geometrical features. We segment the blood vessels from the color retinal RGB image, and apply the morphological thinning operation to find the vessel centerline. Applying a filter on this centreline image we detect the potential bifurcation and crossover points. The geometrical and topological properties of the blood vessels passing through these points are utilized to identify these points as the vessel bifurcations and crossovers. We evaluate our method against manually measured bifurcation and crossover points by an expert, and achieved the detection accuracy of 95.82%.

Blood Vessel Segmentation from Color Retinal Images using Unsupervised Texture Classification

Automated blood vessel segmentation is an important issue for assessing retinal abnormalities and diagnoses of many diseases. The segmentation of vessels is complicated by huge variations in local contrast, particularly in case of the minor vessels. In this paper, we propose a new method of texture based vessel segmentation to overcome this problem. We use Gaussian and L*a*b* perceptually uniform color spaces with original RGB for texture feature extraction on retinal images. A bank of Gabor energy filters are used to analyze the texture features from which a feature vector is constructed for each pixel. The fuzzy C-means (FCM) clustering algorithm is used to classify the feature vectors into vessel or non-vessel based on the texture properties. From the FCM clustering output we attain the final output segmented image after a post processing step. We compare our method with hand-labeled ground truth segmentation of five images and achieve 84.37% sensitivity and 99.61% specificity.

Automated Analysis of Retinal Vascular Tortuosity on Color Retinal Images

Recent advances in medical imaging modality have enabled us to identify new features in retinal vasculature. One of the features is retinal vascular tortuosity which has been shown to become a predictive factor for cardiovascular diseases and diabetes. The changes in retinal vascular tortuosity might be a sign of severity or improvement of the disease. In this paper, we propose a new method for measuring retinal vascular tortuosity. We adopt a new technique to analyze tortuosity that consider vessel-segment’s width simultaneously. Our proposed method measures vessel-segment’s tortuosity on its edge. A qualitative assessment shows that the method is appropriate for measuring the tortuosity of the vessels in different widths and directions in the image. Finally, a comparison distinguishing tortuous vs. non tortuous vessels demonstrates that the proposed approach may be suitable for predicting or earlier diagnosis of diabetes or cardiovascular diseases.

Vessel Cross-Sectional Diameter Measurement on Color Retinal Image

Vessel cross-sectional diameter is an important feature for analyzing retinal vascular changes. In automated retinal image analysis, the measurement of vascular width is a complex process as most of the vessels are few pixels wide or suffering from lack of contrast. In this paper, we propose a new method to measure the retinal blood vessel diameter which can be used to detect arteriolar narrowing, arteriovenous (AV) nicking, branching coefficients, etc. to diagnose various diseases. The proposed method utilizes the vessel centerline and edge information to measure the width for a vessel cross-section. Using the Adaptive Region Growing (ARG) segmentation technique we obtain the edges of the blood vessels, and then applying the unsupervised texture classification method we segment the blood vessels from where the vessel centerline is obtained. The potential pixels pairs for each centerline pixel are obtained from the edge image that pass through this centerline pixel. We apply a rotational invariant mask to search the pixel pairs from the edge image, and calculate the shortest distance pair which provides the vessel width (or diameter) for that cross-section. The method is evaluated with manually measured width for different vessels’ cross-sectional area. For the automated measurement of vascular width we achieve an average accuracy of 95.8%.

Anti-personnel Mine Detection and Classification Using GPR Image

The automated anti-personnel mine (APM) detection and classification is currently a broad issue. The detection success depends on the feature selection that we obtain from the sensors. Ground penetrating radar (GPR) is one of the established sensors for detecting buried APM. In this paper, we introduce a method which improves the accuracy of detecting APM by using GPR imaging. This method adopts a segmentation technique for feature extraction and neural network as a pattern classifier. A seeded region growing algorithm is applied as region based segmentation for pattern construction following the median filtering and threshold of the original GPR image. A feed forward neural network (FFNN) with backpropagation training is employed for classifying the patterns. The FFNN takes the patterns (APM signature) that are constructed from each salient region and generate the classification. This method significantly improves accuracy in the detection and classification of APM

Automatic white matter lesion segmentation using contrast enhanced FLAIR intensity and Markov Random Field

White matter lesions (WMLs) are small groups of dead cells that clump together in the white matter of brain. In this paper, we propose a reliable method to automatically segment WMLs. Our method uses a novel filter to enhance the intensity of WMLs. Then a feature set containing enhanced intensity, anatomical and spatial information is used to train a random forest classifier for the initial segmentation of WMLs. Following that a reliable and robust edge potential function based Markov Random Field (MRF) is proposed to obtain the final segmentation by removing false positive WMLs. Quantitative evaluation of the proposed method is performed on 24 subjects of ENVISion study. The segmentation results are validated against the manual segmentation, performed under the supervision of an expert neuroradiologist. The results show a dice similarity index of 0.76 for severe lesion load, 0.73 for moderate lesion load and 0.61 for mild lesion load. In addition to that we have compared our method with three state of the art methods on 20 subjects of Medical Image Computing and Computer Aided Intervention Societys (MICCAIs) MS lesion challenge dataset, where our method shows better segmentation accuracy compare to the state of the art methods. These results indicate that the proposed method can assist the neuroradiologists in assessing the WMLs in clinical practice.

An Automated Method for Retinal Arteriovenous Nicking Quantification From Color Fundus Images

Retinal arteriovenous (AV) nicking is one of the prominent and significant microvascular abnormalities. It is characterized by the decrease in the venular caliber at both sides of an artery-vein crossing. Recent research suggests that retinal AV nicking is a strong predictor of eye diseases such as branch retinal vein occlusion and cardiovascular diseases such as stroke. In this study, we present a novel method for objective and quantitative AV nicking assessment. From the input retinal image, the vascular network is first extracted using the multiscale line detection method. The crossover point detection method is then performed to localize all AV crossing locations. At each detected crossover point, the four vessel segments, two associated with the artery and two associated with the vein, are identified and two venular segments are then recognized through the artery-vein classification method. The vessel widths along the two venular segments are measured and analyzed to compute the AV nicking severity of that crossover. The proposed method was validated on 47 high-resolution retinal images obtained from two population-based studies. The experimental results indicate a strong correlation between the computed AV nicking values and the expert grading with a Spearman correlation coefficient of 0.70. Sensitivity was 77% and specificity was 92% (Kappa κ = 0.70) when comparing AV nicking detected using the proposed method to that detected using a manual grading method, performed by trained photographic graders.

Drusen Detection and Quantification for Early Identification of Age Related Macular Degeneration using Color Fundus Imaging

Objective: To develop a method for detecting drusen and quantifying drusen size in macular region from standard color retinal images for early diagnosis of Age related Macular Degeneration (AMD). Materials and methods: Color retinal images were used which were captured by Canon D60 non-mydriatic camera for genetic and epidemiology study. Local intensity distribution, adaptive intensity thresholding and edge information were used to detect potential drusen areas. For validation, we considered 50 images with various types of drusen. For the drusen area segmentation accuracy (DAA), 12 images were selected, and an expert grader marked the drusen regions in pixel level. We then quantified the areas and compute the sensitivity and specificity by comparing the drusen detected output images with the hand-labeled ground truth (GT) images. Results: The proposed method detected the presence of any drusen with 100% accuracy (50/50 images). For drusen detection accuracy (pixel level), mean sensitivity and specificity values of 74.94% and 81.17%, respectively. For drusen subtypes we achieved 79.59% accuracy in intermediate drusen and 82.14% in soft drusen which is a highly significant result for early and intermediate AMD detection. Conclusion: In this study, we applied a novel automated method for drusen detection and quantification which is ready to be used in initial screening of early stage of AMD and drusen area changes i.e., AMD progression. The method will also be highly suitable for telemedicine platforms in ophthalmology for selecting patient from rural areas using fundus imaging - for refereeing to an expert ophthalmologist.