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Salient object detection method for breast tumor in ultrasound images based on absorbing Markov chain

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单位: [1]Department of Biomedical Engineering, College of Materials Science and Engineering, Sichuan University, Chengdu, China [2]Department of Ultrasound, West China Hospital of Sichuan University, Chengdu, China [3]China-Japan Friendship Hospital, Beijing, China
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关键词: Ultrasound image breast tumor automatic detection absorbing Markov chain saliency model adaptive selective replacement

摘要:
BACKGROUND: Automatic detection of tumor in breast ultrasound (BUS) images is important for the subsequent image processing and has been researched for decades. However, there still lacks a robust method due to poor quality of BUS images. OBJECTIVE: To propose and test a salient object detection method for BUS images. METHODS: BUS image is preprocessed by an adaptively selective replacement and speckle reducing anisotropic diffusion (SRAD) algorithm. Then, the preprocessed image is segmented into super pixels by a simple linear iterative clustering (SLIC) algorithm to form a graph model, and the saliency of the nodes in the graph is calculated by using the absorbed time of absorbing Markov chain (AMC). Finally, the initial saliency map is optimized by the recurrent time of ergodic Markov chain (EMC) and a distance weighting formula. RESULTS: Results of the proposed method were compared both qualitatively and quantitatively with two saliency detection models. It was observed that the proposed method outperformed the comparison models and yielded the highest Accuracy value (97.49% vs. 86.63% and 90.33%) using a dataset of 1000 BUS images. CONCLUSIONS: After the adaptively selective replacement, AMC can effectively distinguish tumors from background by random walks.

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出版当年[2018]版:
大类 | 4 区 医学
小类 | 4 区 仪器仪表 4 区 光学 4 区 物理:应用
最新[2025]版:
大类 | 4 区 医学
小类 | 4 区 仪器仪表 4 区 光学 4 区 物理:应用
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出版当年[2017]版:
Q3 OPTICS Q3 PHYSICS, APPLIED Q3 INSTRUMENTS & INSTRUMENTATION
最新[2023]版:
Q3 INSTRUMENTS & INSTRUMENTATION Q3 OPTICS Q3 PHYSICS, APPLIED

影响因子: 最新[2023版] 最新五年平均[2021-2025] 出版当年[2017版] 出版当年五年平均[2013-2017] 出版前一年[2016版] 出版后一年[2018版]

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第一作者单位: [1]Department of Biomedical Engineering, College of Materials Science and Engineering, Sichuan University, Chengdu, China
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通讯机构: [1]Department of Biomedical Engineering, College of Materials Science and Engineering, Sichuan University, Chengdu, China [*1]Department of Biomedical Engineering, Sichuan University, Chengdu, 610065 China
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