Retinal Fundus image segmentation is challenging due to the presence of faintly interrelated optic nerve disk, fovea, and blood vessels. Among them most relevant and region of interest is blood vessels for ophthalmologists for proper disease diagnostic purpose. In this paper, we proposed a computationally effective image segmentation algorithm for fundus image segmentation known as Cuckoo Searc...

texture image analysis is one of the most important working realms of imageprocessing in medical sciences and industry. up to present, different approacheshave been proposed for segmentation of texture images. in this paper, we offeredunsupervised texture image segmentation based on markov random field (mrf)model. first, we used gabor filter with different parameters’ (frequency,orientation) va...

This paper presents a novel approach to automatic classifying and identifying of tree leaves using image segmentation fusion. With the development of mobile devices and remote access, automatic plant identification in images taken in natural scenes has received much attention. Image segmentation plays a key role in most plant identification methods, especially in complex background images. Wher...

Texture image analysis is one of the most important working realms of image processing in medical sciences and industry. Up to present, different approaches have been proposed for segmentation of texture images. In this paper, we offered unsupervised texture image segmentation based on Markov Random Field (MRF) model. First, we used Gabor filter with different parameters’ (frequency, orientatio...

With the advancement of CAD (Computer Assisted Diagnosis) Image Processing in Medical Field has created its own niche. Retinal Blood Vessels are highly complicated with low contrast and of very thin diameter which often causes failure in diagnosis of exact abnormalities. However, many researchers have worked on enhancement and segmentation of Retinal Blood Vessels through various methods over t...

The paper address the problem of retinal image segmentation, and aims at describing a novel segmentation technique, based on the watershed algorithm, able to separate in a clear way blood vessels from background. Automatic accurate analysis and quantitative characterization of diagnostically relevant features of retinal images can help clinicians for diagnosis and follow-up of eye diseases.

This paper presents an automatic application that provides several retinal image analysis functionalities, namely vessel segmentation, vessel width estimation, artery/vein classification and optic disc segmentation. A pipeline of these methods allows the computation of important vessel related indexes, namely the Central Retinal Arteriolar Equivalent (CRAE), Central Retinal Venular Equivalent (...

This paper presents Deep Retinal Image Understanding (DRIU), a unified framework of retinal image analysis that provides both retinal vessel and optic disc segmentation. We make use of deep Convolutional Neural Networks (CNNs), which have proven revolutionary in other fields of computer vision such as object detection and image classification, and we bring their power to the study of eye fundus...