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Analysis of tumor vascularity using three-dimensional power Doppler ultrasound images

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dc.contributor.authorHuang, Sheng-Fang-
dc.contributor.authorChang, Ruey-Feng-
dc.contributor.authorMoon, Woo Kyung-
dc.contributor.authorLee, Yu-Hau-
dc.contributor.authorChen, Dar-Ren-
dc.contributor.authorSuri, Jasjit S-
dc.date.accessioned2010-06-28-
dc.date.available2010-06-28-
dc.date.issued2008-03-13-
dc.identifier.citationIEEE Trans Med Imaging. vol. 27, no. 3, pp. 320-330en
dc.identifier.issn0278-0062 (Print)-
dc.identifier.urihttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=18334428-
dc.identifier.urihttp://ieeexplore.ieee.org/stampPDF/getPDF.jsp?tp=&arnumber=04359045&isnumber=4456764-
dc.identifier.urihttps://hdl.handle.net/10371/67863-
dc.description.abstractTumor vascularity is an important factor that has been shown to correlate with tumor malignancy and was demonstrated as a prognostic indicator for a wide range of cancers. Three-dimensional (3-D) power Doppler ultrasound (PDUS) offers a convenient tool for investigators to inspect the signals of blood flow and vascular structures in breast cancer. In this paper, a new computer-aided diagnosis (CAD) system for quantifying Doppler ultrasound images based on 3-D thinning algorithm and neural network is proposed. We extracted the skeleton of blood vessels from 3-D PDUS data to facilitate the capturing of morphological changes. Nine features including vessel-to-volume ratio, number of vascular trees, length of vessels, number of branching, mean of radius, number of cycles, and three tortuosity measures, were extracted from the thinning result. Benign and malignant tumors can therefore be differentiated by a score computed by a multilayered perceptron (MLP) neural network using these features as parameters. The proposed system was tested on 221 breast tumors, including 110 benign and 111 malignant lesions. The accuracy, sensitivity, specificity, and positive and negative predictive values were 88.69% (196/221), 91.89% (102/111), 85.45% (94/110), 86.44% (102/118), and 91.26% (94/103), respectively. The Az value of the ROC curve was 0.94. The results demonstrate a correlation between the morphology of blood vessels and tumor malignancy, indicating that the newly proposed method can retrieves a high accuracy in the classification of benign and malignant breast tumors.en
dc.language.isoenen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.subject*Algorithmsen
dc.subjectArtificial Intelligenceen
dc.subjectHumansen
dc.subjectImage Enhancement/methodsen
dc.subjectImage Interpretation, Computer-Assisted/*methodsen
dc.subjectImaging, Three-Dimensional/*methodsen
dc.subjectNeoplasms/*blood supply/*ultrasonographyen
dc.subjectNeovascularization, Pathologic/*ultrasonographyen
dc.subjectPattern Recognition, Automated/methodsen
dc.subjectReproducibility of Resultsen
dc.subjectSensitivity and Specificityen
dc.subjectUltrasonography, Doppler/*methodsen
dc.titleAnalysis of tumor vascularity using three-dimensional power Doppler ultrasound imagesen
dc.typeArticleen
dc.contributor.AlternativeAuthor문우경-
dc.identifier.doi10.1109/TMI.2007.904665-
Appears in Collections:
College of Medicine/School of Medicine (의과대학/대학원)Radiology (영상의학전공)Journal Papers (저널논문_영상의학전공)
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