Hybrid Features and Mediods Classification based Robust Segmentation of Blood Vessels

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dc.contributor.author Amna Waheed
dc.contributor.author M. Usman Akram
dc.contributor.author Shehzad Khalid
dc.contributor.author Zahra Waheed
dc.contributor.author Muazzam A Khan
dc.contributor.author Arslan Shaukat
dc.date.accessioned 2017-11-22T12:42:08Z
dc.date.available 2017-11-22T12:42:08Z
dc.date.issued 2015
dc.identifier.uri http://hdl.handle.net/123456789/5053
dc.description.abstract Retinal blood vessels are the source to provide oxygen and nutrition to retina and any change in the normal structure may lead to different retinal abnormalities. Automated detection of vascular structure is very important while designing a computer aided diagnostic system for retinal diseases. Most popular methods for vessel segmentation are based on matched filters and Gabor wavelets which give good response against blood vessels. One major drawback in these techniques is that they also give strong response for lesion (exudates, hemorrhages) boundaries which give rise to false vessels. These false vessels may lead to incorrect detection of vascular changes. In this paper, we propose a new hybrid feature set along with new classification technique for accurate detection of blood vessels. The main motivation is to lower the false positives especially from retinal images with severe disease level. A novel region based hybrid feature set is presented for proper discrimination between true and false vessels. A new modified m-mediods based classification is also presented which uses most discriminating features to categorize vessel regions en_US
dc.language.iso en en_US
dc.publisher Bahria University Islamabad Campus en_US
dc.subject Department of Computer Engineering CE en_US
dc.title Hybrid Features and Mediods Classification based Robust Segmentation of Blood Vessels en_US
dc.type Article en_US


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