A novel fuzzy non-homogeneity measure based kernelized image segmentation for noisy images

The paper proposes a novel non-homogeneity measure based kernelized image segmentation algorithm for noisy images. Every 3×3 neighbourhood of every single pixel is considered for generating localized spatial domain non-homogeneity measures for every individual window. Then these spatial domain non-homogeneity measures are converted into fuzzy domain non-homogeneity coefficients by aggregating the localized measures into a single distribution and then deriving fuzzy domain values from a Gaussian membership function. Quantitative analyses have been rendered with respect to state-of-the-art noisy-image segmentation techniques and results show improved performance. Speckle-noise ridden SAR images and Rician noise ridden medical images are finally considered to show real-life applications of our algorithm.