Muhammad Moazam Fraz

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.

An Ensemble Classification-Based Approach Applied to Retinal Blood Vessel Segmentation

This paper presents a new supervised method for segmentation of blood vessels in retinal photographs. This method uses an ensemble system of bagged and boosted decision trees and utilizes a feature vector based on the orientation analysis of gradient vector field, morphological transformation, line strength measures, and Gabor filter responses. The feature vector encodes information to handle the healthy as well as the pathological retinal image. The method is evaluated on the publicly available DRIVE and STARE databases, frequently used for this purpose and also on a new public retinal vessel reference dataset CHASE_DB1 which is a subset of retinal images of multiethnic children from the Child Heart and Health Study in England (CHASE) dataset. The performance of the ensemble system is evaluated in detail and the incurred accuracy, speed, robustness, and simplicity make the algorithm a suitable tool for automated retinal image analysis.

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.

Application of Morphological Bit Planes in Retinal Blood Vessel Extraction

The appearance of the retinal blood vessels is an important diagnostic indicator of various clinical disorders of the eye and the body. Retinal blood vessels have been shown to provide evidence in terms of change in diameter, branching angles, or tortuosity, as a result of ophthalmic disease. This paper reports the development for an automated method for segmentation of blood vessels in retinal images. A unique combination of methods for retinal blood vessel skeleton detection and multidirectional morphological bit plane slicing is presented to extract the blood vessels from the color retinal images. The skeleton of main vessels is extracted by the application of directional differential operators and then evaluation of combination of derivative signs and average derivative values. Mathematical morphology has been materialized as a proficient technique for quantifying the retinal vasculature in ocular fundus images. A multidirectional top-hat operator with rotating structuring elements is used to emphasize the vessels in a particular direction, and information is extracted using bit plane slicing. An iterative region growing method is applied to integrate the main skeleton and the images resulting from bit plane slicing of vessel direction-dependent morphological filters. The approach is tested on two publicly available databases DRIVE and STARE. Average accuracy achieved by the proposed method is 0.9423 for both the databases with significant values of sensitivity and specificity also; the algorithm outperforms the second human observer in terms of precision of segmented vessel tree.

Genetic algorithm based feature selection combined with dual classification for the automated detection of proliferative diabetic retinopathy

Proliferative diabetic retinopathy (PDR) is a condition that carries a high risk of severe visual impairment. The hallmark of PDR is the growth of abnormal new vessels. In this paper, an automated method for the detection of new vessels from retinal images is presented. This method is based on a dual classification approach. Two vessel segmentation approaches are applied to create two separate binary vessel map which each hold vital information. Local morphology features are measured from each binary vessel map to produce two separate 4-D feature vectors. Independent classification is performed for each feature vector using a support vector machine (SVM) classifier. The system then combines these individual outcomes to produce a final decision. This is followed by the creation of additional features to generate 21-D feature vectors, which feed into a genetic algorithm based feature selection approach with the objective of finding feature subsets that improve the performance of the classification. Sensitivity and specificity results using a dataset of 60 images are 0.9138 and 0.9600, respectively, on a per patch basis and 1.000 and 0.975, respectively, on a per image basis.

QUARTZ: Quantitative Analysis of Retinal Vessel Topology and size – An automated system for quantification of retinal vessels morphology

Abstract Retinal vessels are easily and non-invasively imaged using fundus cameras. Growing evidence including longitudinal evidence, suggests morphological changes in retinal vessels are early physio-markers of cardio-metabolic risk and outcome (as well as other disease processes). However, data from large population based studies are needed to examine the nature of these morphological associations. Several retinal image analysis (RIA) systems have been developed. While these provide a number of retinal vessel indices, they are often restricted in the area of analysis, and have limited automation, including the ability to distinguish between arterioles and venules. With the aim of developing reliable, automated, efficient retinal image analysis (RIA) software, generating a rich quantification of retinal vasculature in large volumes of fundus images, we present QUARTZ (Quantitative Analysis of Retinal Vessel Topology and size), a novel automated system for processing and analysing retinal images. QUARTZ consists of modules for vessel segmentation, width measurement and angular change at each vessel centreline pixel with sub-pixel accuracy, computing local vessel orientation, optic disc localisation, arteriole/venule classification, tortuosity measurement, and exporting the quantitative measurements in various output file formats. The performance metrics of the algorithms incorporated in QUARTZ are validated on a number of publically available retinal databases (including DRIVE, STARE, CHASE_DB1, INSPIRE-AVR, and DIARETDB1). QUARTZ performs well in terms of segmentation accuracy, calibre measurement, optic disc and arteriole/venule recognition. The system provides a rich quantification of retinal vessel morphology, which has potential medical applications in identifying those at high risk, so that prophylactic measure can be initiated before onset of overt disease.

Retinal vessel extraction using first-order derivative of Gaussian and morphological processing

The change in morphology, diameter, branching pattern and/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 by means of a unique combination of differential filtering and morphological processing. The centerlines are extracted by the application of first order derivative of Gaussian in four orientations and then the evaluation of derivative signs and average derivative values is made. The shape and orientation map of the blood vessel is obtained by applying a multidirectional morphological top-hat operator followed by bit plane slicing of a vessel enhanced grayscale image. The centerlines are combined with these maps to obtain the segmented vessel tree. The approach is tested on two publicly available databases and results show that the proposed algorithm can obtain robust and accurate vessel tracings with a performance comparable to other leading systems.

Evaluation of retinal vessel segmentation methodologies based on combination of vessel centerlines and morphological processing

The categorization of retinal vessels morphological features and the investigation of their branching patterns are used in the process for automated diagnosis and screening of ophthalmologic diseases. Mathematical morphology has been materialized as a proficient technique for quantifying the retinal vasculature in ocular fundus images. In this paper, the performance comparison of two retinal vessel segmentation approaches based on combination of multi scale morphological reconstruction and morphological bit plane slicing with the vessel centerlines is presented. The segmentation accuracy and the processing time are taken as the performance criteria. These approaches are tested on two publicly available databases and results demonstrate that morphological bit plane slicing outperforms other approach in respect of processing time without a significant degradation of sensitivity and specificity.

Optic disc detection and boundary extraction in retinal images

With the development of digital image processing, analysis and modeling techniques, automatic retinal image analysis is emerging as an important screening tool for early detection of ophthalmologic disorders such as diabetic retinopathy and glaucoma. In this paper, a robust method for optic disc detection and extraction of the optic disc boundary is proposed to help in the development of computer-assisted diagnosis and treatment of such ophthalmic disease. The proposed method is based on morphological operations, smoothing filters, and the marker controlled watershed transform. Internal and external markers are used to first modify the gradient magnitude image and then the watershed transformation is applied on this modified gradient magnitude image for boundary extraction. This method has shown significant improvement over existing methods in terms of detection and boundary extraction of the optic disc. The proposed method has optic disc detection success rate of 100%, 100%, 100% and 98.9% for the DRIVE, Shifa, CHASE_DB1, and DIARETDB1 databases, respectively. The optic disc boundary detection achieved an average spatial overlap of 61.88%, 70.96%, 45.61%, and 54.69% for these databases, respectively, which are higher than currents methods.

A supervised method for retinal blood vessel segmentation using line strength, multiscale Gabor and morphological features

The change in morphology, diameter, branching pattern and/or tortuosity of retinal blood vessels is an important indicator of various clinical disorders of the eye and the body. This paper reports a supervised methodology for segmentation of the retinal vasculature from ocular fundus images. A 7-D feature vector is constructed by computing the outputs of morphological linear operators, line strengths and oriented Gabor filters at multiple scales. The feature vector encodes the spatial intensity measures along with vessel geometry at multiple scales. A Bayesian Classifier; the Gaussian Mixture Model is used for classification of the retinal image into vessels and non-vessel pixels. The methodology is evaluated using the images of two publicly available databases, the DRIVE database and the STARE database. Method performance on both sets of test images is better than the 2nd human observer and other existing methodologies available in the literature.