@Article{10798587.2016.1272778,
AUTHOR = {Li-Hong Juang, Ming-Ni Wu},
TITLE = {Tumor Classfication UsingG Automatic Multi-thresholding},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {24},
YEAR = {2018},
NUMBER = {2},
PAGES = {257--266},
URL = {http://www.techscience.com/iasc/v24n2/39752},
ISSN = {2326-005X},
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.},
DOI = {10.1080/10798587.2016.1272778}
}