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Purpose: Surface curvatures are important geometric features for the computer-aided analysis and detection of polyps in CT colonography (CTC). However, the general kernel approach for curvature computation can yield erroneous results for small polyps and for polyps that lie on haustral folds. Those erroneous curvatures will reduce the performance of polyp detection. This paper presents an analysis of interpolation's effect on curvature estimation for thin structures and its application on computer-aided detection of small polyps in CTC. Methods: The authors demonstrated that a simple technique, image interpolation, can improve the accuracy of curvature estimation for thin structures and thus significantly improve the sensitivity of small polyp detection in CTC. Results: Our experiments showed that the merits of interpolating included more accurate curvature values for simulated data, and isolation of polyps near folds for clinical data. After testing on a large clinical data set, it was observed that sensitivities with linear, quadratic B-spline and cubic Bspline interpolations significantly improved the sensitivity for small polyp detection. Conclusions:The image interpolation can improve the accuracy of curvature estimation for thin structures and thus improve the computer-aided detection of small polyps in CTC.

This paper describes a feasibility study of a method for delineating the tentorium cerebelli in MRI brain scans. The tentorium cerebelli is a thin sheet of dura matter covering the cerebellum and separating it from the posterior part of the temporal lobe and the occipital lobe of the cerebral hemispheres. Cortical structures such as the parahippocampal gyrus can be indistinguishable from tentorium in MPRAGE and T1 weighted magnetic resonance image scans. Similar intensities in these neighboring regions make it difficult to perform accurate cortical analysis in neuroimaging studies of schizophrenia and Alzheimer's disease. A semi-automated, geometric, intensity-based procedure for delineating the tentorium from a whole brain scan is described. Initial and final curves are traced within the tentorium. A cost function, based on intensity and Euclidean distance, is computed between the two curves using the Fast Marching method. The initial curve is then evolved to the final curve based on the gradient of the computed costs, generating a series of intermediate curves. These curves are then used to generate a triangulated surface of the tentorium. For three scans, surfaces were found to be within 2 voxels from hand-segmentations.

CT colonography (CTC) is a feasible and minimally invasive method for the detection of colorectal polyps and cancer screening. Computer-aided detection (CAD) of polyps has improved consistency and sensitivity of virtual colonoscopy interpretation and reduced interpretation burden. A CAD system typically consists of four stages: (1) image preprocessing including colon segmentation; (2) initial detection generation; (3) feature selection; and (4) detection classification. In our experience, three existing problems limit the performance of our current CAD system. First, highdensity orally administered contrast agents in fecal-tagging CTC have scatter effects on neighboring tissues. The scattering manifests itself as an artificial elevation in the observed CT attenuation values of the neighboring tissues. This pseudo-enhancement phenomenon presents a problem for the application of computer-aided polyp detection, especially when polyps are submerged in the contrast agents. Second, general kernel approach for surface curvature computation in the second stage of our CAD system could yield erroneous results for thin structures such as small (6-9 mm) polyps and for touching structures such as polyps that lie on haustral folds. Those erroneous curvatures will reduce the sensitivity of polyp detection. The third problem is that more than 150 features are selected from each polyp candidate in the third stage of our CAD system. These high dimensional features make it difficult to learn a good decision boundary for detection classification and reduce the accuracy of predictions. Therefore, an improved CAD system for polyp detection in CTC data is proposed by introducing three new techniques. First, a scale-based scatter correction algorithm is applied to reduce pseudo-enhancement effects in the image pre-processing stage. Second, a cubic spline interpolation method is utilized to accurately estimate curvatures for initial detection generation. Third, a new dimensionality reduction classifier, diffusion map and local linear embedding (DMLLE), is developed for classification and false positives (FP) reduction. Performance of the improved CAD system is evaluated and compared with our existing CAD system (without applying those techniques) using CT scans of 1186 patients. These scans are divided into a training set and a test set. The sensitivity of the improved CAD system increased 18% on training data at a rate of 5 FPs per patient and 15% on test data at a rate of 5 FPs per patient. Our results indicated that the improved CAD system achieved significantly better performance on medium-sized colonic adenomas with higher sensitivity and lower FP rate in CTC.