detecting blood vessels is an important task in retinal image analysis.  the task is more challenging with the presence of bright and dark lesions in retinal images. here, a method is proposed to detect vessels in both normal and abnormal retinal fundus images based on their linear features. first, the negative impact of bright lesions is reduced by using k-means segmentation in a perceptive sp...

optical coherence tomography (oct) is a powerful imaging modality used to image various aspects of biological tissues, such as structural information, blood flow, elastic parameters, change of polarization states, and molecular content [1]. in contrast to oct technology development which has been a field of active research since 1991, oct image segmentation has only been more fully explored dur...

to investigate the effect of preprocessing techniques including contrast enhancement and illumination correction on retinal imagequality, a comparative study was carried out. we studied and implemented a few illumination correction and contrast enhancementtechniques on color retinal images to find out the best technique for optimum image enhancement. to compare and choose thebest illumination c...

In this paper we consider the problem of blood vessel segmentation in retinal images. After enhancing the retinal image we use green channel of images for segmentation as it provides better discrimination between vessels and background. We consider the negative of retinal green channel image as a topographical surface and extract ridge points on this surface. The points with this property are l...

Retinal image processing is a standard medical repetition and offers number of profitable products for physicians. In the field of retinal image processing retinal vessel segmentation is the crucial task for detection of cardiovascular diseases in advance. The recognition of erroneous blood vessel may cause in incorrect clinical diagnosis. Various processing steps are required for the extractio...

distribution of retinal blood vessels (rbvs) in retinal images has an important role in the prevention, diagnosis, monitoring and treatment of diseases, such as diabetes, high blood pressure, or heart disease. therefore, detection of the exact location of rbvs is very important for ophthalmologists. one of the frequently used techniques for extraction of these vessels is region growing-based se...

A supervised method is proposed for automated segmentation of vessels in fundus images of retina. This method is used to detect the retinal diseases by extracting the retinal vasculature utilizing 9-D feature vector based on orientation analysis of gradient vector field, morphological transformation, line strength measures, and Gabor filter responses. The feature vector encodes information to h...

A novel topological segmentation of retinal images represents blood vessels as connected regions in the continuous image plane, having shape-related analytic and geometric properties. This paper presents topological segmentation results from the DRIVE retinal image database [SAN∗04].