Hybrid Features and Mediods Classification based Robust Segmentation of Blood Vessels.
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...
| Publicado en: | Journal of Medical Systems Vol. 39; no. 10; pp. 1 - 15 |
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| Autores principales: | , , , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2015
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=115925174&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 115925174 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Oct2015 vid: 39 iid: 10 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 115925174 115925174 115925174 10.1007/s10916-015-0316-1 115925174 ppf: 1 ppct: 14 formats: fmt: @attributes: type: P tig: atl: Hybrid Features and Mediods Classification based Robust Segmentation of Blood Vessels. aug: au: Waheed, Amna Akram, M. Khalid, Shehzad Waheed, Zahra Khan, Muazzam Shaukat, Arslan affil: Department of Computer Engineering College of Electrical & Mechanical Engineering, National University of Sciences & Technology, Rawalpindi Pakistan sug: subj: Blood Vessels Analysis Image Processing, Computer Assisted Methods Retina Analysis Automation Blood Vessels Physiopathology Diagnosis, Computer Assisted Artifacts False Positive Results Classification Databases Retinal Diseases Diagnosis Validity Digital Imaging Discriminant Analysis Algorithms Image Processing, Computer Assisted Evaluation Comparative Studies Funding Source ab: 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. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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