Segmentation of Kidney in 3D-Ultrasound Images Using Gabor-based Appearance Models

This paper presents a new segmentation method for 3D ultrasound images of the pediatric kidney. Based on the popular active shape models, the algorithm is tailored to deal with the particular challenges raised by US images. First, a weighted statistical shape model allows to compensate the image variation with the propagation direction of the US wavefront. Second, an orientation correction approach is used to create a Gabor- based appearance model for each landmark at different scales. This multiscale characteristic is incorporated into the segmentation algorithm, creating a hierarchical approach where different appearance models are considered as the segmentation process evolves. The performance of the algorithm was evaluated on a dataset of 14 cases, both healthy and pathological, obtaining an average Dice's coefficient of 0.85, an average point-to-point distance of 4.07 mm, and 0.12 average relative volume difference.