Probability density difference-based active contour for ultrasound image segmentation

Authors:
Bo Liu;H. D. Cheng;Jianhua Huang;Jiawei Tian;Xianglong Tang;Jiafeng Liu
Affiliations:
School of Computer Science and Technology, Harbin Institute of Technology, Harbin PR China;School of Computer Science and Technology, Harbin Institute of Technology, Harbin PR China and Department of Computer Science, Utah State University, Logan, UT 84322, USA;School of Computer Science and Technology, Harbin Institute of Technology, Harbin PR China;Second Affiliated Hospital of Harbin Medical University, Harbin PR China;School of Computer Science and Technology, Harbin Institute of Technology, Harbin PR China;School of Computer Science and Technology, Harbin Institute of Technology, Harbin PR China
Venue:
Pattern Recognition
Year:
2010

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Abstract

Because of its low signal/noise ratio, low contrast and blurry boundaries, ultrasound (US) image segmentation is a difficult task. In this paper, a novel level set-based active contour model is proposed for breast ultrasound (BUS) image segmentation. At first, an energy function is formulated according to the differences between the actual and estimated probability densities of the intensities in different regions. The actual probability densities are calculated directly. For calculating the estimated probability densities, the probability density estimation method and background knowledge are utilized. The energy function is formulated with level set approach, and a partial differential equation is derived for finding the minimum of the energy function. For performing numerical computation, the derived partial differential equation is approximated by the central difference and non-re-initialization approach. The proposed method was operated on both the synthetic images and clinical BUS images for studying its characteristics and evaluating its performance. The experimental results demonstrate that the proposed method can model the BUS images well, be robust to noise, and segment the BUS images accurately and reliably.