A colon polyp phantom, 28 cm long and 5 cm in diameter, was constructed by inflating a latex ultrasound transducer cover. Four round pieces of ham (3, 6, 9, 12 mm diameter) were imbedded in the outer membrane surface of the phantom and then were tied by string at the base to simulate pedunculated polyps. Three more pieces of ham (3, 6, 9 mm) were impressed and taped on the outer surface to simulate sessile polyps. The circumference of the phantom was constricted by string at four evenly spaced locations to simulate haustral folds. The phantom was placed in a water bath and was modified by infusing water into the lumen or by partially deflating the lumen, and then rescanned. CT images were obtained in a multi-slice CT (4x1 mm collimation, 0.5s scan, 120 Kvp, 90 mAs, 1 mm slice thickness). CT images were processed with our computer-aided detection program. First, the three-dimensional colonic boundary and inner structure were segmented. From this segmented region, soft-tissue structures were extracted and labeled to generate candidates. Shape features were evaluated along with geometric constraints. Three-dimensional region-growing and morphologic matching processes were applied to refine and classify the candidates. The detected polyps were compared with the true polyps in the phantom or known polyps in clinical cases to calculate the sensitivity and false positives.
Multi-row detector CT (MDCT) gated with ECG-tracing allows continuous image acquisition of the heart during a breath-hold with a high spatial and temporal resolution. Dynamic segmentation and display of CT images, especially short- and long-axis view, is important in functional analysis of cardiac morphology. The size of dynamic MDCT cardiac images, however, is typically very large involving several hundred CT images and thus a manual analysis of these images can be time-consuming and tedious. In this paper, an automatic scheme was proposed to segment and reorient the left ventricular images in MDCT. Two segmentation techniques, deformable model and region-growing methods, were developed and tested. The contour of the ventricular cavity was segmented iteratively from a set of initial coarse boundary points placed on a transaxial CT image and was propagated to adjacent CT images. Segmented transaxial diastolic cardiac phase MDCT images were reoriented along the long- and short-axis of the left ventricle. The axes were estimated by calculating the principal components of the ventricular boundary points and then confirmed or adjusted by an operator. The reorientation of the coordinates was applied to other transaxial MDCT image sets reconstructed at different cardiac phases. Estimated short-axes of the left ventricle were in a close agreement with the qualitative assessment by a radiologist. Preliminary results from our methods were promising, with a considerable reduction in analysis time and manual operations.