Segmentation of the internal organs in medical images is a difficult task. By incorporating a priori information regarding specific organs of interest, results of segmentation may be improved. Landmarking (i.e., identifying stable structures to aid in gaining more knowledge concerning contiguous structures) is a promising segmentation method. Specifically, segmentation of the diaphragm may help in limiting the scope of segmentation methods to the abdominal cavity; the diaphragm may also serve as a stable landmark for identifying internal organs, such as the liver, the spleen, and the heart. A method to delineate the diaphragm is proposed in the present work. The method is based upon segmentation of the lungs, identification of the lower surface of the lungs as an initial representation of the diaphragm, and the application of least-squares modeling and deformable contour models to obtain the final segmentation of the diaphragm. The proposed procedure was applied to nine X-ray computed tomographic (CT) exams of four pediatric patients with neuroblastoma. The results were evaluated against the boundaries of the diaphragm as identified independently by a radiologist. Good agreement was observed between the results of segmentation and the reference contours drawn by the radiologist, with an average mean distance to the closest point of 5.85 mm over a total of 73 CT slices including the diaphragm.

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Identification, localization, and segmentation of the thoracic, abdominal, and pelvic organs are important steps in computer-aided diagnosis, treatment planning, landmarking, and content-based retrieval of biomedical images. In this context, to aid the identification of the lower abdominal organs, to assist in imageguided surgery or treatment planning, to separate the abdominal cavity from the lower pelvic region, and to improve the process of localization of abdominal pathology, we propose methods to identify and segment automatically the pelvic girdle in pediatric computed tomographic (CT) images. The opening-by-reconstruction procedure was used for segmentation of the pelvic girdle. The methods include procedures to represent the pelvic surface by a quadratic model using linear least-squares estimation and to refine the model using deformable contours. The result of segmentation of the pelvic girdle was assessed quantitatively and qualitatively by comparing with the segmentation performed independently by a radiologist. On the basis of quantitative analysis with 13 CT exams of six patients, including a total of 277 slices with the pelvis, the average Hausdorff distance was determined to be 5.95 mm, and the average mean distance to the closest point (MDCP) was 0.53 mm. The average MDCP is comparable to the size of one pixel, on the average.