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 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.

Quantification of posterior capsular opacification in digital images after cataract surgery.

PURPOSE: To describe a software program developed to provide an objective assessment of the amount of posterior capsular opacification (PCO) in high-resolution digital images of the posterior capsule after cataract surgery. METHODS: Images are analyzed by a set protocol of defining the area of the posterior capsule, removing the Purkinje light reflexes by intensity segmentation, contrast enhancement, filtering to enhance low-density PCO, and variance analysis using a co-occurrence matrix to assess texture. The accuracy of the system was tested for validity and repeatability. RESULTS: The software developed has been demonstrated to be an objective method of quantifying PCO. In validation tests, the image analysis-derived measure of PCO showed good agreement with clinically derived measures of PCO. Clinicians assessed PCO on a computer screen image and also under slit lamp examination (Pearson correlation coefficient for both methods >0.92). The entire acquisition and analysis system was demonstrated to have a confidence limit for 2 SDs of 9.8% for group data. CONCLUSIONS: This system is capable of producing an accurate and reproducible measure of PCO that is relevant to assessing techniques of PCO prevention.