Unsupervised delineation of the vessel tree in retinal fundus images

Abstract

Retinal imaging has gained particular popularity as it provides an opportunity to diagnose various medical pathologies in a non-invasive way. One of the basic and very important steps in the analysis of such images is the delineation of the vessel tree from the background. Such segmentation facilitates the investigation of the morphological characteristics of the vessel tree and the analysis of any lesions in the background, which are both indicators for various pathologies. We propose a novel method called B-COSFIRE for the delineation of the vessel tree. It is based on the classic COSFIRE approach, which is a trainable nonlinear filtering method. The responses of a B-COSFIRE filter is achieved by combining the responses of difference-of-Gaussians filters whose areas of support are determined in an automatic configuration step. We configure two types of B-COSFIRE filters, one that responds selectively along vessels and another that is selective to vessel endings. The segmentation of the vessel tree is achieved by summing up the response maps of both types of filters followed by thresholding. We demonstrate high effectiveness of the proposed approach by performing experiments on four public data sets, namely DRIVE, STARE, CHASE DB1 and HRF. The delineation approach that we propose also has lower time complexity than existing methods.

Learning representations of sound using trainable COPE feature extractors

“…The concept of trainable filters has been previously introduced for visual pattern recognition. COSFIRE filters were proposed for contour detection [67], keypoint and object detection [68], retinal vessel segmentation [69,70], curvilinear structure delineation [71,72,73], and action recognition [74]. In this work, we Representation learning is typical of recent machine learning methods based on deep and convolutional neural networks, which require large amount of training data.…”

Learning audio and image representations with bio-inspired trainable feature extractors

“…We proposed B-COSFIRE (that stands for Bar-selective Combination of Shifted Filter Responses) filters for detection of elongated and curvilinear patterns in images and apply them to the delineation of blood vessels in retinal images [1,8]. The B-COSFIRE filters are trainable, that is their structure is automatically configured from prototype elongated patterns.…”

Brain-Inspired Robust Delineation Operator

“…In this paper, we present a novel filter, inspired by the push-pull inhibition that is exhibited by some neurons in area V1 of the primary visual cortex. We construct a filter that has two components, an excitatory and an inhibitory one, based on the existing model of neurons with excitatory receptive fields in area V1, called CORF [4], whose implementation is known as B-COSFIRE and shown to be effective for the delineation of blood vessels in medical images [5,6], also in combination with machin learnig techniques [7]. We name the proposed filter RUSTICO, which stands for RobUST Inhibition-augmented Curvilinear Operator, and show how push-pull inhibition contributes to strenghten the robustness with respect to noise and spurious texture in the delineation of elongated patterns.…”