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.
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Cheng, DC., Jiang, X., Schmidt-Trucksäss, A. (2007). Image Segmentation Using Histogram Fitting and Spatial Information. In: Perner, P., Salvetti, O. (eds) Advances in Mass Data Analysis of Signals and Images in Medicine, Biotechnology and Chemistry. MDA 2007. Lecture Notes in Computer Science(), vol 4826. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76300-0_5
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DOI: https://doi.org/10.1007/978-3-540-76300-0_5
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