A method for the automatic 3-D segmentation of the spinal canal in computed tomographic (CT) images is presented. The method uses a priori radiological and anatomical information, mathematical morphology, and region-growing methods. The skin and peripheral fat structures are determined by delineating the air and other materials external to the body. Using the fat layer as a reference, the bone structure is segmented. The Hough transform for the detection of circles is applied to a cropped bone edge map that includes the thoracic vertebral structure. The centers of the detected circles are used to derive the information required for the fuzzy connectivity algorithm that is employed to segment the spinal canal. In a preliminary study, the method successfully segmented the spinal canal in eight CT volumes of four patients, with Hausdorff distances with reference to contours drawn independently by a radiologist in the range 1.7±0.8 mm.
Tumor definition and diagnosis require the analysis of the spatial distribution and Hounsfield unit (HU) values of voxels in computed tomography (CT) images, coupled with a knowledge of normal anatomy. Segmentation of the tumor in neuroblastoma is complicated by the fact that the mass is almost always heterogeneous in nature; furthermore, viable tumor, necrosis, fibrosis, and normal tissue are often intermixed. Rather than attempt to separate these tissue types into distinct regions, we propose to explore methods to delineate the normal structures expected in abdominal CT images, remove them from further consideration, and examine the remaining parts of the images for the tumor mass. We explore the use of fuzzy connectivity for this purpose. Expert knowledge provided by the radiologist in the form of the expected structures and their shapes, HU values, and radiological characteristics are also incorporated in the segmentation algorithm. Segmentation and analysis of the tissue composition of the tumor can assist in quantitative assessment of the response to chemotherapy and in the planning of delayed surgery for resection of the tumor. The performance of the algorithm is evaluated using cases acquired from the Alberta Children's Hospital.