LV Contour Extraction Using Difference of Gaussian Weighting Function and Random Walk Approach

Image segmentation is the first step prior to any medical analysis. With the increase in modern disease variety, the images (specially cardiac magnetic resonance (CMR) images) to be segmented are found complex in nature. That might be due to noise, color geometry etc. Random walk method is proved to be good enough to this type of images. Simultaneously, it is robust noise and it does not require any pre-condition to perform. In the present paper we show the importance of weighting function, that is used in the method, on the algorithm output. This paper presents a new approach using difference of Gaussian (DoG) weighting function in the random walk method. We compare the frequently used Gaussian weighting function with DoG and show DoG to be the better one. Finally using DoG weighting function, the random walk method is performed on CMR data for left ventricle contour extraction. The result using DoG weighting function is found to be encouraging than that of Gaussian weighting function.