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

Sheng Fang Huang, Ruey Feng Chang, Woo Kyung Moon, Yu Hau Lee, Dar Ren Chen, Jasjit S. Suri

Research output: Contribution to journalArticlepeer-review

59 Citations (Scopus)

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.

Original languageEnglish
Article number4359045
Pages (from-to)320-330
Number of pages11
JournalIEEE Transactions on Medical Imaging
Volume27
Issue number3
DOIs
Publication statusPublished - 2008 Mar

All Science Journal Classification (ASJC) codes

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering

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