Mahsa Lotfollahi1,2, Masoumeh Gity3, Jing Yong Ye4, A Mahlooji Far5. 1. Department of Electrical Engineering, University of Houston, Houston, TX, USA. mlotfollahiSohi@uh.edu. 2. Department of Biomedical Engineering, Tarbiat Modarres University, Tehran, Iran. mlotfollahiSohi@uh.edu. 3. The Radiology Department, Tehran University of Medical Science, Tehran, Iran. 4. Department of Biomedical Engineering, The University of Texas at San Antonio, San Antonio, TX, USA. 5. Department of Biomedical Engineering, Tarbiat Modarres University, Tehran, Iran.
Abstract
PURPOSE: Ultrasound imaging is an effective approach for diagnosing breast cancer, but it is highly operator-dependent. Recent advances in computer-aided diagnosis have suggested that it can assist physicians in diagnosis. Definition of the region of interest before computer analysis is still needed. Since manual outlining of the tumor contour is tedious and time-consuming for a physician, developing an automatic segmentation method is important for clinical application. METHODS: The present paper represents a novel method to segment breast ultrasound images. It utilizes a combination of region-based active contour and neutrosophic theory to overcome the natural properties of ultrasound images including speckle noise and tissue-related textures. First, due to inherent speckle noise and low contrast of these images, we have utilized a non-local means filter and fuzzy logic method for denoising and image enhancement, respectively. This paper presents an improved weighted region-scalable active contour to segment breast ultrasound images using a new feature derived from neutrosophic theory. RESULTS: This method has been applied to 36 breast ultrasound images. It generates true-positive and false-positive results, and similarity of 95%, 6%, and 90%, respectively. CONCLUSION: The purposed method indicates clear advantages over other conventional methods of active contour segmentation, i.e., region-scalable fitting energy and weighted region-scalable fitting energy.
PURPOSE: Ultrasound imaging is an effective approach for diagnosing breast cancer, but it is highly operator-dependent. Recent advances in computer-aided diagnosis have suggested that it can assist physicians in diagnosis. Definition of the region of interest before computer analysis is still needed. Since manual outlining of the tumor contour is tedious and time-consuming for a physician, developing an automatic segmentation method is important for clinical application. METHODS: The present paper represents a novel method to segment breast ultrasound images. It utilizes a combination of region-based active contour and neutrosophic theory to overcome the natural properties of ultrasound images including speckle noise and tissue-related textures. First, due to inherent speckle noise and low contrast of these images, we have utilized a non-local means filter and fuzzy logic method for denoising and image enhancement, respectively. This paper presents an improved weighted region-scalable active contour to segment breast ultrasound images using a new feature derived from neutrosophic theory. RESULTS: This method has been applied to 36 breast ultrasound images. It generates true-positive and false-positive results, and similarity of 95%, 6%, and 90%, respectively. CONCLUSION: The purposed method indicates clear advantages over other conventional methods of active contour segmentation, i.e., region-scalable fitting energy and weighted region-scalable fitting energy.
Entities:
Keywords:
Active contour; Breast ultrasound image; Neutrosophic theory; Segmentation
Authors: Leila Saadatifard; Louise C Abbott; Laura Montier; Jokubas Ziburkus; David Mayerich Journal: Front Neuroanat Date: 2018-04-26 Impact factor: 3.856