Semi-automatic Breast Ultrasound Image Segmentation Based on Mean Shift and Graph Cuts

Abstract

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.

Real-time envelope detection of ultrasound radiofrequency signals using OpenCV GPU framework

“…The OpenCV GPU framework is implemented based on the CUDA platform and is easy and flexible to use. OpenCV has been used in various ultrasound applications including breast ultrasound image segmentation [14] and quasi-real-time ultrasound backscattered statistical parameter imaging (i.e., ultrasound Nakagami imaging) [15]. However, in our previous work, the envelope detection of RF signals was realized on CPU [15], limiting the frame rate of ultrasound Nakagami imaging (1-3 frames per second).…”

“…OpenCV is an open-source computer vision library written with C/C++ (http://opencv.org/) and has been widely used for image processing and computer vision applications [13][14][15][16]. Specifically, OpenCV has provided a graphics processing unit (GPU) framework for real-time data processing [16].…”

Automatic Nonlinear Filtering and Segmentation for Breast Ultrasound Images

“…Previous works [4][5][6][7][8] described different segmentation methods to discriminate bright lesions within BUS images. It should be noted that a fair comparison between methods is hard to make since results are based on different datasets.…”

“…In the final step, the NC is applied to merge the over-segmented regions and to segment the ROI. A semi-automatic approach is introduced by Zhou et al [8] to segment lesions in BUS images, Gaussian filter and histogram equalization are utilized to smooth and to enhance image contrast. Then, a pyramid mean shift filtering is applied to improve the homogeneity of the enhanced image.…”

A Novel Segmentation Approach Combining Region- and Edge-Based Information for Ultrasound Images

“…Zhang et al [28] applied the discriminative graph-cut approach to segmenting tumors after discrimination between tumors and the background via a trained classifier. In 2014, Zhou et al [29] proposed a novel US image segmentation method based on mean shift and graph cuts (MSGC). It uses mean shift filter to improve the homogeneity and applies graph-cut method, whose energy function combines region- and edge-based information to segment US images.…”