An Automated Neural-Fuzzy Approach to Malignant Tumor Localization in 2D Ultrasonic Images of the Prostate.
In this paper, a new neural-fuzzy approach is proposed for automated region segmentation in transrectal ultrasound images of the prostate. The goal of region segmentation is to identify suspicious regions in the prostate in order to provide decision support for the diagnosis of prostate cancer. The...
| Publicado en: | Journal of Digital Imaging Vol. 24; no. 3; pp. 411 - 424 |
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| Autores principales: | , , , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2011
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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=104894847&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104894847 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Jun2011 vid: 24 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104894847 60502973 10.1007/s10278-010-9301-x NLM20532587 104894847 ppf: 411 ppct: 13 formats: fmt: @attributes: type: P tig: atl: An Automated Neural-Fuzzy Approach to Malignant Tumor Localization in 2D Ultrasonic Images of the Prostate. aug: au: Mohamed, Samar Li, J. Salama, M. Freeman, G. Tizhoosh, H. Fenster, A. Rizkalla, K. affil: Department of Electrical and Computer Engineering, University of Waterloo, 619 Honeywood place Waterloo Canada N2T 2T6 sug: subj: Prostatic Neoplasms Diagnosis Prostatic Neoplasms Ultrasonography Radiographic Image Interpretation, Computer-Assisted Human Algorithms Automation Neural Networks (Computer) ROC Curve Sensitivity and Specificity ab: In this paper, a new neural-fuzzy approach is proposed for automated region segmentation in transrectal ultrasound images of the prostate. The goal of region segmentation is to identify suspicious regions in the prostate in order to provide decision support for the diagnosis of prostate cancer. The new automated region segmentation system uses expert knowledge as well as both textural and spatial features in the image to accomplish the segmentation. The textural information is extracted by two recurrent random pulsed neural networks trained by two sets of data (a suspicious tissues' data set and a normal tissues' data set). Spatial information is captured by the atlas-based reference approach and is represented as fuzzy membership functions. The textural and spatial features are synthesized by a fuzzy inference system, which provides a binary classification of the region to be evaluated. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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