A novel breast ultrasound image segmentation algorithm based on neutrosophic similarity score and level set.
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.
Department of Computer Science, University of Illinois at Springfield, Springfield, IL, USA. Electronic address: yanhui.guo@aggiusu.edu.
Yanhui
Y
Guo
Department of Electric and Electronics Engineering, Technology Faculty, Firat University, Elazig, Turkey.
Abdulkadir
A
Şengür
Department of Ultrasound, Second Affiliated Hospital of Harbin Medical, Harbin, Heilongjiang, China.
Jia-Wei
JW
Tian
D000465:Algorithms; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D020000:Decision Support Systems, Clinical; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D008491:Medical Informatics Applications; D010363:Pattern Recognition, Automated / Q000379:methods / Q000706:statistics & numerical data; D016217:Ultrasonography, Mammary / Q000379:methods* / Q000706:statistics & numerical data
Breast ultrasound; Image segmentation; Level set; Neutrosophic set; Similarity score