Automatic superpixel-based segmentation method for breast ultrasound images. (1st May 2019)

• Record Type: Journal Article
• Authors: Daoud, Mohammad I.; Atallah, Ayman A.; Awwad, Falah; Al-Najjar, Mahasen; Alazrai, Rami
• Is Part Of: Expert systems with applications. Volume 121(2019)
• Page Start: 78
• Page End: 96
• Publication Date: 2019-05-01
• Subjects: Ultrasound breast images -- Superpixels -- Graph cuts segmentation -- Active contour models -- Support vector machine -- Breast cancer diagnosis; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2018.11.024
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: A two-phase, superpixel-based method is proposed for segmenting BUS images. The first phase employs coarse superpixels to achieve effective tumor detection. The second phase uses fine superpixels to enable accurate tumor segmentation. Both phases employ edge- and region-based information to segment the superpixels. The results show that the method enables automatic and accurate tumor segmentation. Abstract: Automatic and accurate breast ultrasound (BUS) image segmentation is crucial to achieve effective ultrasound-based computer aided diagnosis (CAD) systems for breast cancer. However, segmenting the tumor in BUS images is often challenging due to several artifacts that degrade the quality of ultrasound images. In this study, a new two-phase method is proposed to enable automatic and accurate segmentation of BUS images by decomposing the image into superpixels with high boundary recall ratio and employing edge- and region-based information to outline the tumor. The first phase of the method obtains an initial outline of the tumor by decomposing the BUS image into coarse superpixels to enable effective estimation of their tumor likelihoods and employing a customized graph cuts algorithm to segment the superpixels. The segmentation of the superpixels is carried out using edge-based information that quantifies the image contour cue and region-based information that characterizes the texture content of the superpixels. In the second phase, the tumor outline is improved by decomposing the BUS image into fine superpixels that enable high boundary recall ratio and employing the customized graph cuts algorithm to segment the superpixel located around the initial tumor outline. Furthermore, an edge-based active contour model is used to smooth the tumor outline. The performance of the proposed method was evaluated using a database that includes 160 BUS images (86 benign and 74 malignant). The results indicate that the first phase of the proposed method was able to detect the tumor in all BUS images and obtain mean values of the true positive ratio (TPR), false positive ratio (FPR), false negative ratio (FNR), similarity ratio (SIR), Hausdorff error (HE), and mean absolute error (ME) equal to 91.68, 11.16, 8.32, 84.52, 17.59, and 4.67, respectively. In fact, the results obtained by the first phase of the proposed method outperform four existing BUS image segmentation algorithms. Moreover, the second phase of the proposed method was able to improve the tumor outlines of the first phase and achieve mean TPR, FPR, FNR, SIR, HE, and ME values of 96.04, 7.99, 3.96, 91.41, 11.66, and 3.65, respectively. These results suggest the feasibility of employing the proposed method, which enables automatic and accurate tumor segmentation in BUS images, to develop effective CAD systems for breast cancer.