Retinal vascular analysis is prominent in diagnosing many diseases like retina diabetes, heart stroke, and retinal damages. Vessel extraction from the retinal images is the crucial part in analyzing and detecting the abnormalities in the retina that predict the nature of the disease. In this paper an effective approach of extracting the blood vessel structures from the retinal images is proposed. The approach is integration of pre processing contrast adjustment process, extraction with segmentation and post processing. The method is tested and evaluated with standard databases which conclude that the proposed approach can be directed to classify and localize the abnormalities. The proposed approach is compared against multiple algorithms and observed that the method could attain an improvement in the accuracy of about 10~15%. The results obtained in this work reveal that the proposed approach could able to attain an accuracy of 97%.
Medical images do contain important and unimportant spatial regions. Compression methods which are capable of reconstructing the image with high quality are required to compress the medical images. For these images, only a portion of it is useful for diagnosis hence a region based coding techniques are significant for compressing and transmission. Extracting a significant region is of great demand since a slighter mistake may leads to wrong diagnosis. This paper is focused on investigating multiple image processing algorithms for medical images. All the images may not contain the same region of interest, so different approaches are supposed to apply for different images. In these three types of medical images were considered like magnetic resonance (MR) brain images, computer tomography (CT) abdomen images and X-ray lung images. In this paper three automatic region of interest extraction algorithms were proposed for different types of images.
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