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

Cited 55 time in Web of Science Cited 59 time in Scopus
Authors
Huang, Sheng-Fang; Chang, Ruey-Feng; Moon, Woo Kyung; Lee, Yu-Hau; Chen, Dar-Ren; Suri, Jasjit S
Issue Date
2008-03-13
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
IEEE Trans Med Imaging. vol. 27, no. 3, pp. 320-330
Keywords
*AlgorithmsArtificial IntelligenceHumansImage Enhancement/methodsImage Interpretation, Computer-Assisted/*methodsImaging, Three-Dimensional/*methodsNeoplasms/*blood supply/*ultrasonographyNeovascularization, Pathologic/*ultrasonographyPattern Recognition, Automated/methodsReproducibility of ResultsSensitivity and SpecificityUltrasonography, Doppler/*methods
Abstract
Tumor 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.
ISSN
0278-0062 (Print)
Language
English
URI
http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=18334428

http://ieeexplore.ieee.org/stampPDF/getPDF.jsp?tp=&arnumber=04359045&isnumber=4456764

https://hdl.handle.net/10371/67863
DOI
https://doi.org/10.1109/TMI.2007.904665
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College of Medicine/School of Medicine (의과대학/대학원)Radiology (영상의학전공)Journal Papers (저널논문_영상의학전공)
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