Current segmentation techniques require user intervention to fine-tune thresholds and parameters, plot initial contours, refine seed placement, and engage in other optimization strategies. This can cause difficulties for physicians trying to use segmentation tools as they may not have the time or resources to overcome steep learning curves. In order to segment volumetric regions from sequential slices of computed tomography (CT) images with minimal user intervention, we propose an algorithm based on volumetric seeded region growing that employs an adaptive and prioritized expansion. This algorithm requires a user only to identify a voxel in an organ to perform volumetric segmentation. This approach overcomes the need to manually select threshold values for specific organs by analyzing the histogram of voxel similarity to automatically determine a stopping criterion. The homogeneity criterion used for region growth in this approach is calculated from volumetric texture descriptors derived from co-occurrence matrices which consider voxel-pairs in a 3-dimensional neighborhood of a given voxel. Preliminary segmentation results of the kidneys, spleen, and liver were obtained on 3D data extracted from 700 sequential CT images from various studies collected by Northwestern Memorial Hospital. We believe this approach to be a viable segmentation technique that requires significantly less user intervention when compared to other techniques by necessitating only one user intervention, namely the selection of a single seed point.
The performance of segmentation algorithms often depends on numerous parameters such as initial seed and contour placement, threshold selection, and other region-dependent <i>a priori</i> 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.