Open Access
ARTICLE
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,
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.
Keywords
Cite This Article
APA Style
Juang, L., Wu, M. (2018). Tumor classfication usingg automatic multi-thresholding. Intelligent Automation & Soft Computing, 24(2), 257-266. https://doi.org/10.1080/10798587.2016.1272778
Vancouver Style
Juang L, Wu M. Tumor classfication usingg automatic multi-thresholding. Intell Automat Soft Comput . 2018;24(2):257-266 https://doi.org/10.1080/10798587.2016.1272778
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. https://doi.org/10.1080/10798587.2016.1272778