NDLI: Hybrid Features and Mediods Classification based Robust Segmentation of Blood Vessels

Content Provider	Springer Nature Link
Author	Waheed, Amna Akram, M. Usman Khalid, Shehzad Waheed, Zahra Khan, Muazzam A Shaukat, Arslan
Copyright Year	2015
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 into true and false vessels. The evaluation of proposed system is done thoroughly on publicly available databases along with a locally gathered database with images of advanced level of retinal diseases. The results demonstrate the validity of the proposed system as compared to existing state of the art techniques.
Starting Page	1
Ending Page	14
Page Count	14
File Format	PDF
ISSN	01485598
Journal	Journal of Medical Systems
Volume Number	39
Issue Number	10
e-ISSN	1573689X
Language	English
Publisher	Springer US
Publisher Date	2015-08-26
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Ocular diseases Shape and intensity based features Mediods based classification False vessels Lesions Health Informatics Statistics for Life Sciences, Medicine, Health Sciences
Content Type	Text
Resource Type	Article
Subject	Health Information Management Information Systems Health Informatics

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