Image segmentation using histogram fitting and spatial information

Authors:
Da-Chuan Cheng;Xiaoyi Jiang;Arno Schmidt-Trucksäss
Affiliations:
Department of Radiological Technology, China Medical University, Taiwan;Department of Mathematics and Computer Science, University of Münster, Germany;Lehrstuhl und Poliklinik für Präventive und Rehabilitative Sportmedizin, Technische Universität München, Germany
Venue:
MDA'06/07 Proceedings of the 2007 international conference on Advances in mass data analysis of signals and images in medicine biotechnology and chemistry
Year:
2007

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Abstract

In this paper, we introduce a novel unsupervised segmentation method using a histogram fitting method to find out the optimal histogram clustering based on multi Gaussian models. The fitting problem is performed via the trust region reflective Newton method to minimize a predefined cost function. The histogram clustering is the global information describing the probability of a given gray value belonging to a category. Together with the consideration of the spatial information, the image segmentation is performed. We demonstrate some applications on medical images such as brain CT and MRI.