Kernelized Weighted SUSAN based Fuzzy C-Means Clustering for Noisy Image Segmentation

TL;DR

This paper introduces a kernelized fuzzy clustering method for noisy image segmentation that enhances edge preservation and structural detail recovery, validated through extensive qualitative and quantitative evaluations on various noisy images.

Contribution

It presents a novel kernelized fuzzy C-means algorithm incorporating SUSAN-based spatial constraints and an edge quality metric for improved noisy image segmentation.

Findings

Effective in preserving edges and details in noisy images

Outperforms state-of-the-art algorithms in qualitative and quantitative tests

Successfully applied to SAR and MRI images with different noise types

Abstract

The paper proposes a novel Kernelized image segmentation scheme for noisy images that utilizes the concept of Smallest Univalue Segment Assimilating Nucleus (SUSAN) and incorporates spatial constraints by computing circular colour map induced weights. Fuzzy damping coefficients are obtained for each nucleus or center pixel on the basis of the corresponding weighted SUSAN area values, the weights being equal to the inverse of the number of horizontal and vertical moves required to reach a neighborhood pixel from the center pixel. These weights are used to vary the contributions of the different nuclei in the Kernel based framework. The paper also presents an edge quality metric obtained by fuzzy decision based edge candidate selection and final computation of the blurriness of the edges after their selection. The inability of existing algorithms to preserve edge information and structural details in their segmented maps necessitates the computation of the edge quality factor (EQF) for all the competing algorithms. Qualitative and quantitative analysis have been rendered with respect to state-of-the-art algorithms and for images ridden with varying types of noises. Speckle noise ridden SAR images and Rician noise ridden Magnetic Resonance Images have also been considered for evaluating the effectiveness of the proposed algorithm in extracting important segmentation information.