  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.
