Abstract
In a previous work, we proposed multi-context fuzzy clustering (MCFC) method on the basis of a local tissue distribution model to classify 3D T1-weighted MR images into tissues of white matter, gray matter, and cerebral spinal fluid in the condition of intensity inhomogeneity. This paper is a complementary and improved version of MCFC. Firstly, quantitative analyses are presented to validate the soundness of basic assumptions of MCFC. Carefully studies on the segmentation results of MCFC disclose a fact that misclassification rate in a context of MCFC is spatially dependent on the anatomical position of the context in the brain; moreover, misclassifications concentrate in regions of brain stem and cerebellum. Such unique distribution pattern of misclassification inspires us to choose different size for the contexts at such regions. This anatomy-dependent MCFC (adMCFC) is tested on 3 simulated and 10 clinical T1-weighted images sets. Our results suggest that adMCFC outperforms MCFC as well as other related methods.
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Zhu, C.Z., Lin, F.C., Zhu, L.T., Jiang, T.Z. (2004). Anatomy Dependent Multi-context Fuzzy Clustering for Separation of Brain Tissues in MR Images. In: Yang, GZ., Jiang, TZ. (eds) Medical Imaging and Augmented Reality. MIAR 2004. Lecture Notes in Computer Science, vol 3150. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28626-4_24
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DOI: https://doi.org/10.1007/978-3-540-28626-4_24
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