Calcification segmentation based on a different scales superpixels saliency detection algorithm |
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| Institution: | 2. College of Information and Communication Engineering, Nanjing Institute of Technology, Nanjing, Jiangsu, China 211167;3. Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China 210029;3. Medisys, Philips Research, Suresnes, France;2. Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark;4. Copenhagen Academy for Medical Education and Simulation (CAMES), Capital Region of Denmark, Copenhagen, Denmark;2. Department of Obstetrics and Gynecology, University of Seville, Spain;3. Biostatistics Unit, Department of Preventive Medicine and Public Health, University of Seville, Spain;4. Department of Pathology, Valme University Hospital, Seville, Spain;2. School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA |
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| Abstract: | Accurate detection of breast tumor calcifications is of great significance in assisting doctors’ diagnosis to improve the accuracy of breast cancer early detection. In this article, a different scale of superpixels saliency detection algorithm is used to segment calcifications in breast tumor ultrasound images based on a simple linear iterative cluster. First, a multi-scale saliency segmentation algorithm was used to divide the tumor region of different sizes and weak calcification (Wca) was extracted according to uneven gray distribution and texture contrast between regions. Second, based on single-scale superpixel segmentation of the original image, the strong calcification extraction map was calculated by measuring gray value difference and calcification gray distance features. Finally, the final calcification extraction map was obtained by combining the strong and weak calcification extraction maps. The detection algorithm proposed in this article could effectively detect calcifications in breast ultrasound images. |
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