An image processing pipeline to detect and segment nuclei in muscle fiber microscopic images |
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| Authors: | Yanen Guo Xiaoyin Xu Yuanyuan Wang Yaming Wang Shunren Xia Zhong Yang |
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| Affiliation: | 1. Key laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, China;2. Zhejiang Provincial Key Laboratory of Cardio‐Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, China;3. Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts;4. Department of Anesthesia, Brigham and Women's Hospital, Boston, Massachusetts;5. Department of Clinical Hematology, Southwest Hospital, Third Military Medical University, Chongqing, China |
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| Abstract: | Muscle fiber images play an important role in the medical diagnosis and treatment of many muscular diseases. The number of nuclei in skeletal muscle fiber images is a key bio‐marker of the diagnosis of muscular dystrophy. In nuclei segmentation one primary challenge is to correctly separate the clustered nuclei. In this article, we developed an image processing pipeline to automatically detect, segment, and analyze nuclei in microscopic image of muscle fibers. The pipeline consists of image pre‐processing, identification of isolated nuclei, identification and segmentation of clustered nuclei, and quantitative analysis. Nuclei are initially extracted from background by using local Otsu's threshold. Based on analysis of morphological features of the isolated nuclei, including their areas, compactness, and major axis lengths, a Bayesian network is trained and applied to identify isolated nuclei from clustered nuclei and artifacts in all the images. Then a two‐step refined watershed algorithm is applied to segment clustered nuclei. After segmentation, the nuclei can be quantified for statistical analysis. Comparing the segmented results with those of manual analysis and an existing technique, we find that our proposed image processing pipeline achieves good performance with high accuracy and precision. The presented image processing pipeline can therefore help biologists increase their throughput and objectivity in analyzing large numbers of nuclei in muscle fiber images. Microsc. Res. Tech. 77:547–559, 2014. © 2014 Wiley Periodicals, Inc. |
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| Keywords: | muscle fiber images clustered nuclei Bayesian network identification watershed |
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