Robust segmentation and intelligent decision system for cerebrovascular disease.
Segmentation and classification of low-quality and noisy ultrasound images is challenging task. In this paper, a new approach is proposed for robust segmentation and classification of carotid artery ultrasound images and consequently, detecting cerebrovascular disease. The proposed technique consist...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 54; no. 12; pp. 1903 - 1921 |
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| Autores principales: | , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Dec2016
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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=119385014&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 119385014 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Dec2016 vid: 54 iid: 12 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 119385014 119385014 NLM27056410 10.1007/s11517-016-1481-1 NLM27056410 119385014 ppf: 1903 ppct: 18 formats: fmt: @attributes: type: P tig: atl: Robust segmentation and intelligent decision system for cerebrovascular disease. aug: au: Chaudhry, Asmatullah Hassan, Mehdi Khan, Asifullah affil: DMIS , PAEC-HQ , Islamabad Pakistan sug: subj: Cerebrovascular Disorders Diagnosis Image Interpretation, Computer Assisted Decision Making Algorithms ROC Curve Logic Ultrasonics Carotid Intima-Media Thickness Databases Carotid Arteries Ferrans and Powers Quality of Life Index ab: Segmentation and classification of low-quality and noisy ultrasound images is challenging task. In this paper, a new approach is proposed for robust segmentation and classification of carotid artery ultrasound images and consequently, detecting cerebrovascular disease. The proposed technique consists of two phases, in first phase; it refines the class labels selected by user using expectation maximization algorithm. Genetic algorithm is then employed to select discriminative features based on moments of gray-level histogram. The selected features and refined targets are fed as input to neuro-fuzzy classifier for performing segmentation. Finally, intima-media thickness values are measured from segmented images to segregate the normal and abnormal subjects. In second phase, an intelligent decision-making system based on support vector machine is developed to utilize the intima-media thickness values for detecting cerebrovascular disease. The proposed robust segmentation and classification technique for ultrasound images (RSC-US) has been tested on a dataset of 300 real carotid artery ultrasound images and yields accuracy, F-measure, and MCC scores of 98.84, 0.988, 0.9767 %, respectively, using jackknife test. The segmentation and classification performance of the proposed (RSC-US) has been also tested at several noise levels and may be used as secondary observation. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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