Hybrid learning of vessel segmentation in retinal images

Abstract

In this paper, a novel technique of vessel segmentation in retinal images using a hybrid learning based approach is proposed. Unlike most other existing methods, a double-layer segmentation technique combining supervised and instance learning steps is introduced to enhance a sensitivity score of segmenting retinal blood vessels. The supervised learning based approach alone may not cope with unseen patterns caused by intrinsic variations in shapes, sizes, and color intensities of blood vessels across different retinal images. Thus, in the proposed hybrid learning solution, the supervised learning part is adopted to compute initial seeds of segmented vessels. They are then fed into the instance learning part as an initial foreground to further learn specific characteristics of vessels in each individual image. In the supervised learning step, the support vector machine (SVM) is applied on three types of features including green intensity, line operators, and Gabor filters. An iterative graph cut is adopted in the instance learning step, together with the pre-processing of morphological operations and the watershed algorithm. The proposed method is evaluated using two well-known datasets, DRIVE and STARE. It shows promising sensitivity scores of 82.6% and 82.0% on the DRIVE and STARE datasets respectively, and outperforms other existing methods in the literature.