Ophthalmology can profit greatly from the analysis of digital images because they can aid in establishing an early diagnosis even before the first symptoms appear. This dissertation [1] contributes to the digital analysis of such images and the problems that arise along the imaging pipeline of fundus photography, a field that is commonly referred to as retinal image analysis. We have dealt with...

In this paper, we present a supervised framework for extracting blood vessels from retinal images. The local standardisation of the green channel of the retinal image and the Gabor filter responses at four different scales are used as features for pixel classification. The Bayesian classifier is used with a bagging framework to classify each image pixel as vessel or background. A post processin...

Retinal image analysis is done through different diagnosis methods in this modern Ophthalmology .Many novel methods are present to segment blood vessels and optic disc in the fundus retinal images. This methods are used for non-intrusive diagnosis since in the modern ophthalmology ,morphology of blood vessel and optic disc is an important indicator for some of the diseases like diabetic retinop...

Background and Objectives: Identification of surgical instruments in laparoscopic video images has several biomedical applications. While several methods have been proposed for accurate detection of surgical instruments, the accuracy of these methods is still challenged high complexity of the laparoscopic video images. This paper introduces a Surgical Instrument Detection Framework (SIDF) for a...

Retinal blood vessel segmentation and analysis is critical for the computer-aided diagnosis of different diseases such as diabetic retinopathy. This study presents an automated unsupervised method segmenting retinal vasculature based on hybrid methods. The algorithm initially applies a preprocessing step using morphological operators to enhance tree structure against non-uniform image backgroun...

A software application is presented, for the detection and measurement of retinal vessels in fundoscopy images. This software is comprised by a measurement module and a Graphical User Interface (GUI) module. The measurement module employs image processing methods and operates in batch mode, to segment retinal vessels in the input image. In addition, the measurement module estimates the diameter...

This system proposes a new supervised approach for the blood vessel segmentation method in retina image. This proposed system overcomes the problem of segmenting thin vessels. This method uses a Fuzzy Neural Network (FNN) scheme for pixel classification and computes a 7-D vector composed of gray-level, moment invariants-based features for pixel representation and AM-FM method for composition of...

Although retinal vessel segmentation has been extensively researched, a robust and time efficient segmentation method is highly needed. This paper presents a local adaptive thresholding technique based on gray level cooccurrence matrix- (GLCM-) energy information for retinal vessel segmentation. Different thresholds were computed using GLCM-energy information. An experimental evaluation on DRIV...

Segmentation and localization of fundus image is a crucial step of pathologies in diagnosing the retinal diseases. Swelling in different parts of vasculature, as change in width along blood vessels and tortuosity may lead to eye-blindness. This process can be utilized in automated screening of the patients suffering from diabetic retinopathy. An attempt was made to apply texture based Gabor fil...

PURPOSE To describe an approach to the use of optical coherence tomography (OCT) imaging in large, population-based studies, including methods for OCT image acquisition, storage, and the remote, rapid, automated analysis of retinal thickness. METHODS In UK Biobank, OCT images were acquired between 2009 and 2010 using a commercially available "spectral domain" OCT device (3D OCT-1000, Topcon)....