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Tumor Classfication UsingG Automatic Multi-thresholding

Li-Hong Juanga, Ming-Ni Wub

a School of Electrical Engineering and Automation, Xiamen University of Technology, No.600, Ligong Road, Jimei, Xiamen, 360124, p.R.China;
b Department of Information management, national taichung university of technology, taichung, taiwan RoC

* Corresponding Author: li-Hong Juang, email

Intelligent Automation & Soft Computing 2018, 24(2), 257-266.


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.


Cite This Article

APA Style
Juang, L., Wu, M. (2018). Tumor classfication usingg automatic multi-thresholding. Intelligent Automation & Soft Computing, 24(2), 257-266.
Vancouver Style
Juang L, Wu M. Tumor classfication usingg automatic multi-thresholding. Intell Automat Soft Comput . 2018;24(2):257-266
IEEE Style
L. Juang and M. Wu, "Tumor Classfication UsingG Automatic Multi-thresholding," Intell. Automat. Soft Comput. , vol. 24, no. 2, pp. 257-266. 2018.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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