Amiya Halder
ABSTRACT
This paper proposes an Enhanced Kernelized Conditional Spatial Fuzzy C Means (EKCSFCM) clustering algorithm targeted at segmentation of Brain Magnetic Resonance (MR) image data. The approach amalgamates kernel mapping with a spatially relevant fuzzy clustering paradigm for image segmentation. A technique for rank-based consideration of spatial information is introduced and is used along with spatial tuning parameters for controlling neighborhood effects during data point clustering. The kernelized approach and the spatial parameters handle intensity inhomogeneities and strengthen the performance of the algorithm by improving resiliency against noise and other aberrations. For analyzing the robustness of the approach, four volumes of brain MR data, each comprising 71 images are used. These images are also corrupted with noise and inhomogeneities for evaluating the efficiency of different approaches in noisy image segmentation. The experimentation results along with the qualitative and quantitative inferences verify the robustness of the algorithm across a wide variety of test cases.
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Index Terms
- Enhanced Kernelized Conditional Spatial Fuzzy C Means Algorithm for Noisy Brain MRI Tissue Segmentation
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