Blood vessel segmentation in modern wide-field retinal images in the presence of additive Gaussian noise

Abstract

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.

Multidomain feature fusion method for small object classification: MDFF

“…Our framework is not limited to the improvement of some special spatial models such as ConvMixer and ViT. It can also be applied to enhance other spatial feature models easily, including recognition methods based on contour features, such as (Misra, 2018), 23 (Asem, 2018), 24 and (Saleem, 2019) 25 . Due to the significant differences between the spatial and frequency domain features of small object, our improvement strategy can remarkably enhance the information content of the original features (spatial) by introducing frequency domain features.…”

Multi-Biometric System Based on Cutting-Edge Equipment for Experimental Contactless Verification

“…The adaptive thresholding method was used for segmentation of the bloodstream [13]. The obtained image after thresholding (Figure 5a) corresponded to the bloodstream of large veins.…”

“…Hence, these characteristics can be used to identify and/or verify the person [12]. Moreover, these characteristics can be scanned (excluding the fingerprint) using a camera with a basic resolution (640 x 480 pixels), and saved in biometric memory, unlike the scanning of the eye retina [13] (which may not necessarily be saved) for future references. From the foregone, we can say that it is safe and inexpensive to embark on a project of creating small devices for the scanning biometric characteristics of the hand.…”

Deep convolutional neural network-based patch classification for retinal nerve fiber layer defect detection in early glaucoma

“…Moreover, the blood vessels passing through/near RNFLD acts as obstruction in the detection process. In the preprocessing step, the extracted blood vessels [14][15][16] are removed through the inpainting process. 17 The visibility of RNFLD region is further enhanced by contrast-limited adaptive histogram equalization (CLAHE).…”