Open Access

ARTICLE

Tumor Classfication UsingG Automatic Multi-thresholding

Li-Hong Juang^a, Ming-Ni Wu^b

^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,

Intelligent Automation & Soft Computing 2018, 24(2), 257-266. https://doi.org/10.1080/10798587.2016.1272778

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.

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Keywords

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