Multiple Resolution Bayesian Segmentation of Ultrasound Images |
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| Institution: | 2. Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China;3. Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, Hangzhou, China;4. Zhejiang Chinese Medical University, Hangzhou, China;1. Department of Internal Medicine, Division of Hematology Oncology, UC Davis School of Medicine, United States;2. Department of Internal Medicine, UC Davis School of Medicine, United States;3. Center for Oncology and Hematology Outcomes Research and Training (COHORT), Division of Hematology Oncology, UC Davis School of Medicine, United States;1. Department of Ultrasound, Huashan Hospital, Fudan University, No. 12 Urumqi Middle Road, Shanghai 200040, China;2. Department of Electronic Engineering, Fudan University, No. 220, Handan Road, Shanghai 200433, China;3. Department of Ultrasound, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Shanghai 200032, China;1. Department of Diagnostic and Interventional Radiology, Hôpital Edouard Herriot, Hospices Civils de Lyon, University of Lyon, 69003 Lyon, France;2. Interventional Radiology Oncology Unit, Hôpital Edouard Herriot, Hospices Civils de Lyon, University of Lyon, 69003 Lyon, France;3. Department of Hepatology, Hôpital Edouard Herriot, Hospices Civils de Lyon, University of Lyon, 69003 Lyon, France |
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| Abstract: | We propose a novel method for obtaining the maximum a posteriori (MAP) probabilistic segmentation of speckle-laden ultrasound images. Our technique is multiple-resolution based, and relies on the conversion of speckle images with Rayleigh statistics to subsampled images with Gaussian statistics. This conversion reduces computation time, as well as allowing accurate parameter estimation for a probabilistic segmentation algorithm. Results appear to provide improvements over previous techniques in terms of low-contrast detail and accuracy. |
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