General Retinal Vessel Segmentation Using Regularization-Based Multiconcavity Modeling

Abstract

Abstract-Detecting blood vessels in retinal images with the presence of bright and dark lesions is a challenging unsolved problem. In this paper, a novel multiconcavity modeling approach is proposed to handle both healthy and unhealthy retinas simultaneously. The differentiable concavity measure is proposed to handle bright lesions in a perceptive space. The line-shape concavity measure is proposed to remove dark lesions which have an intensity structure different from the line-shaped vessels in a retina. The locally normalized concavity measure is designed to deal with unevenly distributed noise due to the spherical intensity variation in a retinal image. These concavity measures are combined together according to their statistical distributions to detect vessels in general retinal images. Very encouraging experimental results demonstrate that the proposed method consistently yields the best performance over existing state-of-the-art methods on the abnormal retinas and its accuracy outperforms the human observer, which has not been achieved by any of the state-of-the-art benchmark methods. Most importantly, unlike existing methods, the proposed method shows very attractive performances not only on healthy retinas but also on a mixture of healthy and pathological retinas.

Aligning Scan Acquisition Circles in Optical Coherence Tomography Images of The Retinal Nerve Fibre Layer

“…The 'shaded band' feature of vessels is then detected as the local minimums on the averaged pixel intensities around the RPE (Figure 1(II)). The vessel features in the retinal fundus images differ with the imaging techniques used, and the vessel segmentation in retinal images have been extensively studied previously [20][21][22][23][24][25]. In the implementation of vessel detection for SLP fundus image, a measure of 'vesselness' serves as a pre-processing step for segmentation of vessels in the retinal fundus image.…”

Retinal Vessel Segmentation via Structure Tensor Coloring and Anisotropy Enhancement

“…Jiang and Mojon [11] 20 s Al-Diri et al [60] 11 min Lam et al [61] 13 min Marin et al [22] 1.5 min Fraz et al [62] 2 min Azzopardi et al [52] 10 s Proposed Method 6.22 s…”

Supervised vessel delineation in retinal fundus images with the automatic selection of B-COSFIRE filters

“…In [22], information about the size, orientation, and width of the vessels is exploited by a region growing procedure. A model of the vessels based on their concavity and built by using a differentiable concavity measure was proposed in [18]. In previous works [6,35], we introduced trainable filters selective for vessels and vesselendings.…”