Ocular fundus image assessment has been extensively used by ophthalmologists for diagnosing vascular and non vascular pathology. Examining the retinal blood vessel network may reveal arteriosclerosis, diabetes, hypertension, cardiovascular disease and stroke [12]. Furthermore, the segmentation of the vessel network is the most suitable representation for the retinal image registration since vas...

Retinal vessel segmentation algorithms play an important role for the detection of numerous diseases, such as diabetic retinopathy, glaucoma and many others. The aim of this paper is report an automated method for segmentation of retinal blood vessels using the crest lines and morphological operations. Initially, each image is preprocessed and the green channel is obtained. Then, we extract the...

We first describe how to “fuzzify” the estimated binary columns to create a [0,1]-valued column. Werefer to this [0,1] -valued column as the soft segmentation column of the noisy spectrogram column.Similarly to the collection of soft segmentation columns as the soft segmentation image, or simply asthe soft segmentation. The band-dependent posterior probability that the hard segmentation columnv...

Background: Brain tissue segmentation for delineation of 3D anatomical structures from magnetic resonance (MR) images can be used for neuro-degenerative disorders, characterizing morphological differences between subjects based on volumetric analysis of gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF), but only if the obtained segmentation results are correct. Due to image arti...

Optic disc detection and segmentation is one of the key elements for automatic retinal disease screening systems. The aim of this survey paper is to review, categorize and compare the optic disc detection algorithms and methodologies, giving a description of each of them, highlighting their key points and performance measures. Accordingly, this survey firstly overviews the anatomy of the eye fu...

Medical imaging is very popular research area these days and includes computer aided diagnosis of different diseases by taking digital images as input. Digital retinal images are used for the screening and diagnosis of diabetic retinopathy, an eye disease. An automated system for the diagnosis of diabetic retinopathy should highlight all signs of disease present in the image and in order to imp...

The blood vessel detection and segmentation technique plays a useful clinical role in an automated retinopathy analysis system. But manual segmentation of vessels requires expertise. It is a very tedious and time consuming task. Employing computational intelligence for this purpose would help in efficient retinal analysis. An user friendly graphical user interface (GUI) that is MATLAB based too...

In medical field, diagnosis of diseases competently carried out by using the image processing. So that to retrieve the relevant data from the amalgamation of resulting image is too difficult. Here the segmentation done by semi supervised learning then the result is tuned by using Deep Learning Neural Network. Higher tuning of results will leads to efficient detection of disease. The experiment ...

Diabetic retinopathy is one of the leading disabling chronic diseases, and one of the leading causes of preventable blindness in the world. Early diagnosis of diabetic retinopathy enables timely treatment and in order to achieve it a major effort will have to be invested into screening programs and especially into automated screening programs. For automated screening programs to work robustly e...