Retinal blood vessels indicate some serious health ramifications, such as cardiovascular disease and stroke. Thanks to modern imaging technology, high-resolution images provide detailed information to help analyze retinal vascular features before symptoms associated with such conditions fully develop. Additionally, these retinal images can be used by ophthalmologists to facilitate diagnosis and the procedures of eye surgery. A fuzzy noise reduction algorithm was employed to enhance color images corrupted by Gaussian noise. The present paper proposes employing a contrast limited adaptive histogram equalization to enhance illumination and increase the contrast of retinal images captured from state-of-the-art cameras. Possessing directional properties, the multistructure elements method can lead to high-performance edge detection. Therefore, multistructure elements-based morphology operators are used to detect high-quality image ridges. Following this detection, the irrelevant ridges, which are not part of the vessel tree, were removed by morphological operators by reconstruction, attempting also to keep the thin vessels preserved. A combined method of connected components analysis (CCA) in conjunction with a thresholding approach was further used to identify the ridges that correspond to vessels. The application of CCA can yield higher efficiency when it is locally applied rather than applied on the whole image. The significance of our work lies in the way in which several methods are effectively combined and the originality of the database employed, making this work unique in the literature. Computer simulation results in wide-field retinal images with up to a 200-deg field of view are a testimony of the efficacy of the proposed approach, with an accuracy of 0.9524.
Since the fundamental features of retinal images are comprised different orders and resolution, in this paper, we attempt to analyze retinal images by means of a multiresolution method, which is based on contourlet transform. This impressive transform method is a relatively newly-introduced two-dimensional extension or equivalent of the wavelet transform making use of multi-scale and directional filter banks. Its corresponding expansion, also known as The Contourlet Expansion, consists of basis images being oriented in various directions and in multiple scales with potentially flexible aspect ratios. Movement effects of the eyeball occurring during the scanning process encouraged us to employ a method based on the Radial Tchebichef Moments so as to estimate and eliminate the effects rotation angle of the head or eyeball movement may introduce in the scanning process. After this, localizing the optic disc and eliciting the Region of Interest (ROI) intended to acquire similar parts within different retinal images from the same person, a rotation invariant template can be achieved from each ROI retinal sample. The Mahalanobis distance is utilized in the proposed method to assess the biometric pattern similarity; human identification is achievable though solving the maximization for the matching scores. The experimental results encompassing 5500 images resulted from 550 subjects show EER = 0.0032 in retinal identification. These results are a testimony of the power and efficacy of the proposed approach.
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