期刊
ULTRASOUND IN MEDICINE AND BIOLOGY
卷 38, 期 2, 页码 262-275出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ultrasmedbio.2011.10.022
关键词
Breast ultrasound segmentation; CAD (computer-aided diagnosis); Region-of-interest (ROI); Phase feature; Distance feature
Lesion segmentation is a challenging task for computer aided diagnosis systems. In this article, we propose a novel and fully automated segmentation approach for breast ultrasound (BUS) images. The major contributions of this work are: an efficient region-of-interest (ROI) generation method is developed and new features to characterize lesion boundaries are proposed. After a ROI is located automatically, two newly proposed lesion features (phase in max-energy orientation and radial distance), combined with a traditional intensity-and-texture feature, are utilized to detect the lesion by a trained artificial neural network. The proposed features are tested on a database of 120 images and the experimental results prove their strong distinguishing ability. Compared with other breast ultrasound segmentation methods, the proposed method improves the TP rate from 84.9% to 92.8%, similarity rate from 79.0% to 83.1% and reduces the FP rate from 14.1% to 12.0%, using the same database. In addition, sensitivity analysis demonstrates the robustness of the proposed method. (E-mail: hengda.cheng@usu.edu) (C) 2012 World Federation for Ultrasound in Medicine & Biology.
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