Segmentation is an important step which is required in many image processing applications to extract useful information. In medical imaging field, segmentation is required to identify the disease embedded in particular organ, e.g. it helps in early diagnosis of diseases like glaucoma, diabetic retinopathy and macular degeneration by segmenting retinal images (Sinthanayothin et al., 2002; Ahmed et al., 2008; Dey et al., 2012). Glaucoma is one of the most common diseases and if it is not early detected, may have serious cost, can even lead to eye blindness. Most of the existing detection and assessment methods of diabetic retinopathy are manual, costly affair and also require trained ophthalmologists (Teng et al., 2002; Olson et al., 2003).
Morphological feature of retinal blood vessel may be utilized as a vital sign for various retinal diseases such as diabetes, arteriosclerosis and hypertension. The geometrical changes in veins and arteries of the retinal images can be measured and applied to a variety of clinical studies to develop automatic …show more content…
However, automatic retinal segmentation process suffers from various drawbacks, i.e. the retinal images are often poorly contrasted, noisy and also the widths of retinal vessels may vary from very large scale to very small value. Therefore, in this work the images were preprocessed using adaptive thresholding and contrast enhancement techniques. Detection and assessment of blood vessels has been the area of research in medical imaging for the past few years. This work includes some algorithms that usually use some kind of vessel enhancement before applying segmentation techniques. The current methods producing high accuracy for retinal image with thick vessels requires high computational cost. The use of proposed methods in this work makes it possible to detect these vessels faster, while preserving a high