In this paper, we propose a hybrid approach for automatic single-organ segmentation in computed tomography (CT) data. The approach consists of three stages: first, a probability image of the organ of interest is obtained by applying a binary classification model obtained using pixel-based texture features; second, an adaptive split-and-merge segmentation algorithm is applied on the organ probability image to remove the noise introduced by the misclassified pixels; and third, the segmented organ's boundaries from the previous stage are iteratively refined using a region growing algorithm. While we applied our approach for liver segmentation in 2-D CT images, a challenging and important task in many medical applications, the proposed approach can be applied for the segmentation of any other organ in CT images. Moreover, the proposed approach can be extended to perform automatic multiple organ segmentation and to build context-sensitive reporting tools for computer-aided diagnosis applications.

In this paper, a supervised pixel-based classifier approach for segmenting different anatomical regions in abdominal computed tomography (CT) studies is presented. The approach consists of three steps: texture extraction, classifier creation, and anatomical regions identification. First, a set of co-occurrence texture descriptors is calculated for each pixel from the image data sample; second, a decision tree classifier is built using the texture descriptors and the names of the tissues as class labels. At the conclusion of the classification process, a set of decision rules is generated to be used for classification of new pixels and identification of different anatomical regions by joining adjacent pixels with similar classifications. It is expected that the proposed approach will also help automate different semi-automatic segmentation techniques by providing initial boundary points for deformable models or seed points for split and merge segmentation algorithms. Preliminary results obtained for normal CT studies are presented.