Weighting function in Random Walk based left ventricle segmentation

Cardiac Magnetic Resonance (CMR) image segmentation is a crucial step before physicians go for patient diagnoses, related image guided surgery or medical data visualization. Most of the existing algorithms are effective under certain circumstances. On the other hand, Random Walk approach is robust for image segmentation in every condition. Weighting function plays an important role for a successful segmentation in the approach. In this work, an attempt has been made to study the behavior of the weighting function with respect to the intensity distribution in the object to be segmented. In this work, we present a weighting function viz. derivative of Gaussian, that is proved to yield better segmentation results while applying on ischemic CMR images, where objects are obscure. Virtuous results on CMR images describes the potential of the weighting function.