FCM Based Blood Vessel Segmentation Method for Retinal Images

TL;DR

This paper presents a new FCM-based algorithm for blood vessel segmentation in retinal images, achieving high sensitivity and accuracy, which can aid early diagnosis of ocular diseases like glaucoma and diabetic retinopathy.

Contribution

The work introduces a novel FCM clustering method for retinal blood vessel segmentation and compares its performance against expert annotations.

Findings

Segmentation achieved 99.62% sensitivity.

Specificity was 54.66%.

Overall accuracy was 95.03%.

Abstract

Segmentation of blood vessels in retinal images provides early diagnosis of diseases like glaucoma, diabetic retinopathy and macular degeneration. Among these diseases occurrence of Glaucoma is most frequent and has serious ocular consequences that can even lead to blindness, if it is not detected early. The clinical criteria for the diagnosis of glaucoma include intraocular pressure measurement, optic nerve head evaluation, retinal nerve fiber layer and visual field defects. This form of blood vessel segmentation helps in early detection for ophthalmic diseases, and potentially reduces the risk of blindness. The low-contrast images at the retina owing to narrow blood vessels of the retina are difficult to extract. These low contrast images are, however useful in revealing certain systemic diseases. Motivated by the goals of improving detection of such vessels, this present work proposes an algorithm for segmentation of blood vessels and compares the results between expert ophthalmologist hand-drawn ground-truths and segmented image(i.e. the output of the present work).Sensitivity, specificity, positive predictive value (PPV), positive likelihood ratio (PLR) and accuracy are used to evaluate overall performance.It is found that this work segments blood vessels successfully with sensitivity, specificity, PPV, PLR and accuracy of 99.62%, 54.66%, 95.08%, 219.72 and 95.03%, respectively.