A novel breast ultrasound image segmentation algorithm based on neutrosophic similarity score and level set. Issue 123 (January 2016)

• Record Type: Journal Article
• Authors: Guo, Yanhui; Şengür, Abdulkadir; Tian, Jia-Wei
• Is Part Of: Computer methods and programs in biomedicine. Issue 123(2016)
• Page Start: 43
• Page End: 53
• Publication Date: 2016-01
• Subjects: Breast ultrasound -- Image segmentation -- Neutrosophic set -- Similarity score -- Level set; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2015.09.007
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: This study proposes a novel method to segment the tumor in the breast ultrasound images. The proposed approach combined the neutrosophic similarity score with level set algorithm for image segmentation. Neutrosophic similarity score is employed to deal with uncertain information on image segmentation. Abstract: Breast ultrasound (BUS) image segmentation is a challenging task due to the speckle noise, poor quality of the ultrasound images and size and location of the breast lesions. In this paper, we propose a new BUS image segmentation algorithm based on neutrosophic similarity score (NSS) and level set algorithm. At first, the input BUS image is transferred to the NS domain via three membership subsets T, I and F, and then, a similarity score NSS is defined and employed to measure the belonging degree to the true tumor region. Finally, the level set method is used to segment the tumor from the background tissue region in the NSS image. Experiments have been conducted on a variety of clinical BUS images. Several measurements are used to evaluate and compare the proposed method's performance. The experimental results demonstrate that the proposed method is able to segment the BUS images effectively and accurately.