Qinghua Huang1,2,3, Yaozhong Luo4, Qiangzhi Zhang4. 1. School of Electronics and Information, and Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, Xi'an, 710072, Shaanxi, People's Republic of China. qhhuang@scut.edu.cn. 2. College of Information Engineering, Shenzhen University, Shenzhen, 518060, China. qhhuang@scut.edu.cn. 3. School of Electronic and Information Engineering, South China University of Technology, Guangzhou, 510641, China. qhhuang@scut.edu.cn. 4. School of Electronic and Information Engineering, South China University of Technology, Guangzhou, 510641, China.
Abstract
PURPOSE: Breast cancer is the most common form of cancer among women worldwide. Ultrasound imaging is one of the most frequently used diagnostic tools to detect and classify abnormalities of the breast. Recently, computer-aided diagnosis (CAD) systems using ultrasound images have been developed to help radiologists to increase diagnosis accuracy. However, accurate ultrasound image segmentation remains a challenging problem due to various ultrasound artifacts. In this paper, we investigate approaches developed for breast ultrasound (BUS) image segmentation. METHODS: In this paper, we reviewed the literature on the segmentation of BUS images according to the techniques adopted, especially over the past 10 years. By dividing into seven classes (i.e., thresholding-based, clustering-based, watershed-based, graph-based, active contour model, Markov random field and neural network), we have introduced corresponding techniques and representative papers accordingly. RESULTS: We have summarized and compared many techniques on BUS image segmentation and found that all these techniques have their own pros and cons. However, BUS image segmentation is still an open and challenging problem due to various ultrasound artifacts introduced in the process of imaging, including high speckle noise, low contrast, blurry boundaries, low signal-to-noise ratio and intensity inhomogeneity CONCLUSIONS: To the best of our knowledge, this is the first comprehensive review of the approaches developed for segmentation of BUS images. With most techniques involved, this paper will be useful and helpful for researchers working on segmentation of ultrasound images, and for BUS CAD system developers.
PURPOSE:Breast cancer is the most common form of cancer among women worldwide. Ultrasound imaging is one of the most frequently used diagnostic tools to detect and classify abnormalities of the breast. Recently, computer-aided diagnosis (CAD) systems using ultrasound images have been developed to help radiologists to increase diagnosis accuracy. However, accurate ultrasound image segmentation remains a challenging problem due to various ultrasound artifacts. In this paper, we investigate approaches developed for breast ultrasound (BUS) image segmentation. METHODS: In this paper, we reviewed the literature on the segmentation of BUS images according to the techniques adopted, especially over the past 10 years. By dividing into seven classes (i.e., thresholding-based, clustering-based, watershed-based, graph-based, active contour model, Markov random field and neural network), we have introduced corresponding techniques and representative papers accordingly. RESULTS: We have summarized and compared many techniques on BUS image segmentation and found that all these techniques have their own pros and cons. However, BUS image segmentation is still an open and challenging problem due to various ultrasound artifacts introduced in the process of imaging, including high speckle noise, low contrast, blurry boundaries, low signal-to-noise ratio and intensity inhomogeneity CONCLUSIONS: To the best of our knowledge, this is the first comprehensive review of the approaches developed for segmentation of BUS images. With most techniques involved, this paper will be useful and helpful for researchers working on segmentation of ultrasound images, and for BUS CAD system developers.
Entities:
Keywords:
Breast cancer; Computer-aided diagnosis; Segmentation; Ultrasound
Authors: Benjamin O Anderson; Roman Shyyan; Alexandru Eniu; Robert A Smith; Cheng-Har Yip; Nuran Senel Bese; Louis W C Chow; Shahla Masood; Scott D Ramsey; Robert W Carlson Journal: Breast J Date: 2006 Jan-Feb Impact factor: 2.431
Authors: Kenneth J W Taylor; Christopher Merritt; Catherine Piccoli; Robert Schmidt; Glenn Rouse; Bruno Fornage; Eva Rubin; Dianne Georgian-Smith; Fred Winsberg; Barry Goldberg; Ellen Mendelson Journal: Ultrasound Med Biol Date: 2002-01 Impact factor: 2.998
Authors: Jimena Olveres; Germán González; Fabian Torres; José Carlos Moreno-Tagle; Erik Carbajal-Degante; Alejandro Valencia-Rodríguez; Nahum Méndez-Sánchez; Boris Escalante-Ramírez Journal: Quant Imaging Med Surg Date: 2021-08
Authors: Moi Hoon Yap; Manu Goyal; Fatima M Osman; Robert Martí; Erika Denton; Arne Juette; Reyer Zwiggelaar Journal: J Med Imaging (Bellingham) Date: 2018-10-10