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

By Muhammad Moazam Fraz, Sarah Ann Barman, Paolo Remagnino, Andreas Hoppe, A. Basit, Bunyarit Uyyanonvara, Alicja R. Rudnicka, Christopher G. Owen. Comput. Methods Programs Biomed., 2012

357 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition, Ophthalmology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/c556cc0c2e40490d1b988a7cdd72a468e85ecd5b

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Citations in the corpus

Hierarchical retinal blood vessel segmentation based on feature and ensemble learning (2015, methodology): “Mathematical morphology coupling curvature evaluation [8] and matched filtering for centerline detection [9,10] is also deployed for retinal vessel segmentation.”
DUNet: A deformable network for retinal vessel segmentation (2018, background): “Fraz et al. combined vessel centerlines detection and morphological bit plane slicing to extract vessel from retinal images [8].”
A Cross-Modality Learning Approach for Vessel Segmentation in Retinal Images (2016, background): “The existence of vessel crossing, branching and centerline reflex makes it difficult [1], [8], [9] to segment the vessels accurately using artificially designed features.”
An Ensemble Classification-Based Approach Applied to Retinal Blood Vessel Segmentation (2012, background): “Mathematical morphology in combination with curvature evaluation [9] and matched filtering for centreline detection [10], [11] is also exploited for retinal vessel segmentation.”