Application of Morphological Bit Planes in Retinal Blood Vessel Extraction

Abstract

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.

Retinal Blood Vessel Segmentation Based on Multi-Scale Deep Learning

“…R ETINAL fundus images have been widely used for diagnosis, screening and treatment of cardiovascular and ophthalmologic diseases [1], including age-related macular degeneration(AMD), diabetic retinopathy(DR), glaucoma, hypertension, arteriosclerosis and choroidal neovascularization, among which AMD and DR have been considered as two leading causes of blindness [2]. Vessel segmentation is a basic step for the quantitative analysis of retinal fundus images [3].…”

Improved automated vessel segmentation for diagnosing eye diseases using fundus images

“…Additional information about retinal blood vessel segmentation methods can be found in the survey by Fraz et al [11] A combination of methods for detecting blood vessel skeletons and multidirectional morphological bit plane slicing has been presented to extract blood vessels. [23] However, these combination methods consider only a single classifier, unlike ensemble classification approaches, and the number of features is much larger than the number of samples, which results in less accurate classification results. Additionally, in the segmentation process, most of these algorithms do not include the extraction of the optic nerve head, a region unwanted for segmenting blood vessels.…”

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

“…These issues could be amplified in the presence of severe retinal diseases. In methodology, available approaches could be roughly classified into four broad categories: (1) line or edge tracing, [12][13][14][15][16][17][18][19][20] (2) multiscale filtering, [21][22][23][24][25][26][27] (3) morphological deformation, [28][29][30][31][32] and (4) machine learning based classification. [32][33][34][35][36][37][38] By exploiting the linear structure of retinal vessels, the line or edge tracing method typically identifies a set of initial seed points and then starts from them to progressively trace the vessels.…”