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TUMOR CLASSIFICATION USING AUTOMATIC MULTI-THRESHOLDING


Authors

Li-Hong Juang ()*

School of Electrical Engineering and Automation, Xiamen University of Technology, No.600, Ligong Road, Jimei, 360124, P.R.China

 
Ming-Ni Wu

Department of Information Management, National Taichung University of Technology, Taichung, Taiwan

 


Abstract

In this paper we explore these math approaches for medical image applications. The application of the proposed method for detection tumor will be able to distinguish exactly tumor size and region. In this research, some major design and experimental results of tumor objects detection method for medical brain images is developed to utilize an automatic multi-thresholding method to handle this problem by combining the histogram analysis and the Otsu clustering. The histogram evaluations can decide the superior number of clusters firstly. The Otsu classification algorithm solves the given medical image by continuously separating the input gray-level image by multi-thresholding until reaching optimal smooth rate. The method solves exactly the problem of the uncertain contoured objects in medical image by using the Otsu clustering classification with automatic multi-thresholding operation.


Keywords


Pages

Total Pages: 10
Pages: 257-266

DOI
10.1080/10798587.2016.1272778


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Published

Volume: 24
Issue: 2
Year: 2018

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JOURNAL INFORMATION


ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
10.1080/10798587 with T&F
IMPACT FACTOR: 0.652 (2017/2018)
Journal: 1995-Present




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