Semi-automatic breast ultrasound image segmentation based on mean shift and graph cuts.
Computerized tumor segmentation on breast ultrasound (BUS) images remains a challenging task. In this paper, we proposed a new method for semi-automatic tumor segmentation on BUS images using Gaussian filtering, histogram equalization, mean shift, and graph cuts. The only interaction required was to select two diagonal points to determine a region of interest (ROI) on an input image. The ROI image was shrunken by a factor of 2 using bicubic interpolation to reduce computation time. The shrunken image was smoothed by a Gaussian filter and then contrast-enhanced by histogram equalization. Next, the enhanced image was filtered by pyramid mean shift to improve homogeneity. The object and background seeds for graph cuts were automatically generated on the filtered image. Using these seeds, the filtered image was then segmented by graph cuts into a binary image containing the object and background. Finally, the binary image was expanded by a factor of 2 using bicubic interpolation, and the expanded image was processed by morphological opening and closing to refine the tumor contour. The method was implemented with OpenCV 2.4.3 and Visual Studio 2010 and tested for 38 BUS images with benign tumors and 31 BUS images with malignant tumors from different ultrasound scanners. Experimental results showed that our method had a true positive rate (TP) of 91.7%, a false positive (FP) rate of 11.9%, and a similarity (SI) rate of 85.6%. The mean run time on Intel Core 2.66 GHz CPU and 4 GB RAM was 0.49 ± 0.36 s. The experimental results indicate that the proposed method may be useful in BUS image segmentation.
College of Life Science and Bioengineering, Beijing University of Technology, Beijing, China.
Zhuhuang
Z
Zhou
College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing, China.
Weiwei
W
Wu
College of Life Science and Bioengineering, Beijing University of Technology, Beijing, China wushuicai@bjut.edu.cn tsuiph@mail.cgu.edu.tw.
Shuicai
S
Wu
Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan wushuicai@bjut.edu.cn tsuiph@mail.cgu.edu.tw.
Po-Hsiang
PH
Tsui
Department of Computer Science and Information Engineering, Chang Gung University, Taoyuan, Taiwan.
Chung-Chih
CC
Lin
Department of Biomedical Engineering, Shenzhen University, Shenzhen, Guangdong, China.
Ling
L
Zhang
Department of Biomedical Engineering, Shenzhen University, Shenzhen, Guangdong, China.
Tianfu
T
Wang
D000465:Algorithms; D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D002681:China; D005189:False Positive Reactions; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D016217:Ultrasonography, Mammary / Q000379:methods*
OpenCV; breast ultrasound; graph cuts; mean shift; semi-automatic segmentation