Medical Decision-Making System of Ultrasound Carotid Artery Intima-Media Thickness Using Neural Networks.
The objective of this work is to develop and implement a medical decision-making system for an automated diagnosis and classification of ultrasound carotid artery images. The proposed method categorizes the subjects into normal, cerebrovascular, and cardiovascular diseases. Two contours are extracte...
| Publicado en: | Journal of Digital Imaging Vol. 24; no. 6; pp. 1112 - 1126 |
|---|---|
| Autores principales: | , , |
| Formato: | diagnostic images tables/charts Journal Article |
| Publicado: |
Springer Nature
Dec2011
|
| 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=104596138&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104596138 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Dec2011 vid: 24 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104596138 67104996 10.1007/s10278-010-9356-8 NLM21181487 104596138 ppf: 1112 ppct: 14 formats: fmt: @attributes: type: P tig: atl: Medical Decision-Making System of Ultrasound Carotid Artery Intima-Media Thickness Using Neural Networks. aug: au: Santhiyakumari, N. Rajendran, P. Madheswaran, M. affil: Department of ECE, K.S.R. College of Technology, Tiruchengode 637215 India sug: subj: Carotid Arteries Ultrasonography Diagnosis, Computer Assisted Image Processing, Computer Assisted Methods Decision Support Systems, Clinical Carotid Artery Diseases Diagnosis Neural Networks (Computer) Systems Design Ultrasonography Adult Middle Age Aged Female Male Algorithms Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Female Male ab: The objective of this work is to develop and implement a medical decision-making system for an automated diagnosis and classification of ultrasound carotid artery images. The proposed method categorizes the subjects into normal, cerebrovascular, and cardiovascular diseases. Two contours are extracted for each and every preprocessed ultrasound carotid artery image. Two types of contour extraction techniques and multilayer back propagation network (MBPN) system have been developed for classifying carotid artery categories. The results obtained show that MBPN system provides higher classification efficiency, with minimum training and testing time. The outputs of decision support system are validated with medical expert to measure the actual efficiency. MBPN system with contour extraction algorithms and preprocessing scheme helps in developing medical decision-making system for ultrasound carotid artery images. It can be used as secondary observer in clinical decision making. pubtype: Academic Journal doctype: diagnostic images tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|