Analysis of dynamic object deformations such as cardiac motion is of great importance, especially when there is a necessity to visualize and compare the deformation behavior across subjects. However, there is a lack of effective techniques for comparative visualization and assessment of a collection of motion data due to its 4-dimensional nature, i.e., timely varying three-dimensional shapes. From the geometric point of view, the motion change can be considered as a function defined on the 2D manifold of the surface. This paper presents a novel classification and visualization method based on a medial surface shape space, in which two novel shape descriptors are defined, for discriminating normal and abnormal human heart deformations as well as localizing the abnormal motion regions. In our medial surface shape space, the geodesic distance connecting two points in the space measures the similarity between their corresponding medial surfaces, which can quantify the similarity and disparity of the 3D heart motions. Furthermore, the novel descriptors can effectively localize the inconsistently deforming myopathic regions on the left ventricle. An easy visualization of heart motion sequences on the projected space allows users to distinguish the deformation differences. Our experimental results on both synthetic and real imaging data show that this method can automatically classify the healthy and myopathic subjects and accurately detect myopathic regions on the left ventricle, which outperforms other conventional cardiac diagnostic methods.

A novel segmentation framework for a prepless virtual colonoscopy (VC) is presented, which reduces the necessity for colon cleansing before the CT scan. The patient is injected rectally with a water-soluble iodinated contrast medium using manual insufflators and a small rectal catheter. Compared to the air-based contrast medium, this technique can better preserve the color lumen and reduce the partial volume effect. However, the contrast medium, together with the fecal materials and air, makes colon wall segmentation challenging. Our solution makes no assumptions about the shape, size, and location of the fecal material in the colon. This generality allows us to label the fecal material accurately and extract the colon wall reliably. The accuracy of our technique has been verified on 60 human subjects. Compared with current VC technologies, our method is shown to be better in terms of both sensitivity and specificity. Further, in our experiments, the accuracy of the technique was comparable to that of optical colonoscopy results.

Making use of neighborhood time series is an effective way of noise reduction in fMRI data. However the conventional averaging methods blur activated areas. In this paper, a filter with adaptive kernel is designed such that its kernel size and direction are defined at each voxel. First, for finding the optimum size of kernel, a linear combination of some isotropic Gaussian filters with different variances is used, and optimum value of variance is specified. Then, the appropriate kernel direction is determined by a linear combination of some anisotropic basic filters with various directions. The weights of these linear combinations can be calculated by using the restricted canonical correlation method. The proposed method is compared with a similar method based on the steerable filters ([7]) and the results show that the proposed method improves the ROC curve and prohibits false spread of the activation areas.