Phase based distance regularized level set for the segmentation of ultrasound kidney images

Segmentation of ultrasound images has been considered as a challenging task due to its low contrast characteristics. Expert's manual segmentation not only relies on just intensity information available with the image because human vision system roughly relies on intensity but at the same time, it reads local information too. Hence, an attempt has been made, where local properties such as phase, energy and amplitude were extracted from the gray scale image using monogenic signals. Here, the monogenic signals were constructed using Cauchy functions rather than traditional Log Gabor function. Active contour based segmentation that incorporates distance regularized level set evolution concept has been used to segment the region of interest on the local information based image i.e Phase Based Distance Regularized Level Set. Proposed method utilizes local phase and feature asymmetry in achieving the objective. Robustness of the proposed method is demonstrated with the quantitative metrics such as Dice Similarity Coefficient (DSC) index, Hausdorff Distance (HD) and Mean Absolute Deviation (MAD) measures. Proposed method is compared quantitatively with the intensity based Distance Regularized Level Set Evolution (DRLSE), with Geodesic Active Contour (GAC) and with the variant of the proposed method that uses Log Gabor filter bank instead of Cauchy.