A prioritized and adaptive approach to volumetric seeded region growing using texture descriptors

The performance of segmentation algorithms often depends on numerous parameters such as initial seed and contour placement, threshold selection, and other region-dependent a priori knowledge. While necessary for successful segmentation, appropriate setting of these parameters can be difficult to achieve and requires a user experienced with the algorithm and knowledge of the application field. In order to overcome these difficulties, we propose a prioritized and adaptive volumetric region growing algorithm which will automatically segment a region of interest while simultaneously developing a stopping criterion. This algorithm utilizes volumetric texture extraction to establish the homogeneity criterion by which the analysis of the aggregating voxel similarities will, over time, define region boundaries. Using our proposed approach on a volume, derived from Computed Tomography (CT) images of the abdomen, we segmented three organs of interest (liver, kidney and spleen). We find that this algorithm is capable of providing excellent volumetric segmentations while also demanding significantly less user intervention than other techniques as it requires only one interaction from the user, namely the selection of a single seed voxel.