Abstract
Structural changes in retinal blood vessels are important indicators for many ocular diseases including diabetic retinopathy (DR), glaucoma, and hypertension. An automated vessel segmentation process almost always begins with acquired color fundus image, containing three layers of images (red, green and blue), and then quickly converted them to a single grayscale image. However, grayscale conversion is not unique and more than one grayscale representation can be obtained for a given color image. For the many currently existing automated vessel extraction methods, the green channel of the RGB color fundus image is routinely used as an input grey-scale representation to a pipeline of the segmentation process for the reason that it provides the best contrast among all three channels, namely red, green and blue. We hypothesize that vessel information contained in dropped channels, red and blue, will add to result in improved segmentation performance. In this paper, we propose a linear combination framework to utilize all three channels of the color fundus image based on their importance level rather than completely discarding some channels. We devised a Principal Component Analysis (PCA) method that provides appropriate weights for each color channel to realize a more discriminative grey-scale representation. The added information made available through PCA-based grayscale representation results in improved performance for Modautomated vessel segmentation algorithms. The performance of the framework is analyzed on two publically available databases (DRIVE, STARE) of fundus images quantifying improvements in all three aspects called accuracy, sensitivity, and specificity.
Highlights
Structural changes in retinal blood vessel network is a good indicator of the presence of ophthalmic diseases, e.g., hypertension, cardiovascular diseases, diabetic retinopathy, glaucoma, etc. [1, 2]
Vessel segmentation of fundus images has played a major role in calibrating change that helps measure the severity of ocular diseases
Given that it is possible to produce more than one gray-scale representation for the same color image, it is important that we find a color-to-grayscale conversion method that results in optimum contrast between the vascular networks and the image background
Summary
Structural changes in retinal blood vessel network is a good indicator of the presence of ophthalmic diseases, e.g., hypertension, cardiovascular diseases, diabetic retinopathy, glaucoma, etc. [1, 2]. Among them the multi-scale line filtering as proposed in [4] has proven to be more effective in detecting thin low-contrast vessels. In these approaches, classification of the vessel or non-vessel pixels is decided without training process, i.e., training data do not contribute to finding the model parameter. We should concentrate on finding an appropriate mapping method during the conversion from three-dimensional color images into one-dimensional gray images so as to minimize the loss of relevant information. The PCA-based color-to-gray image conversion method is supposed to effectively preserves and, in some cases, increase the discriminability between vessel and non-vessel classes in color image by simple linear computations in appropriate subspaces. We can retain the best possible gray-scale representation having high classification value with low computational complexity
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