Gallbladder quantification in ultrasound using GVF snakes

Accurate identification of the gallbladder contour on ultrasound images is a first step towards reporting quantitative measures of gallbladder function. We present a semi-automatic method for segmentation of gallbladder on ultrasound images. We propose a technique for computing feature images in ultrasound that is based on the hessian computed within a multi-scale framework. We show how the proposed feature image is suited for evolving an active contour subject to the Gradient Vector Flow (GVF) energy. Qualitative results on real-world data show that the proposed approach is capable of segmenting both longitudinal and transverse images of the gall-bladder. Quantitative analysis of segmentation accuracy demonstrates a target overlap of 0.93 ± 0.02 and dice coefficient of 0.94 ± 0.01 with the ground truth.