In this study we introduce a novel algorithm for learning a polyhedron to describe the target class. The proposed approach takes advantage of the limited subclass information made available for the negative samples and jointly optimizes multiple hyperplane classifiers each of which is designed to classify positive samples from a subclass of the negative samples. The flat faces of the polyhedron provides robustness whereas multiple faces contributes to the flexibility required to deal with complex datasets. Apart from improving the prediction accuracy of the system, the proposed polyhedral classifier also provides run-time speedups as a by-product when executed in a cascaded framework in real-time. We evaluate the performance of the proposed technique on a real-world Colon dataset both in terms of prediction accuracy and online execution speed.

We present a space leaping technique for accelerating volume rendering with very low space and run-time complexity. Our technique exploits the ray coherence during ray casting by using the distance a ray traverses in empty space to leap its neighboring rays. Our technique works with parallel as well as perspective volume rendering, does not require any preprocessing or 3D data structures, and is independent of the transfer function. Being an image-space technique, it is independent of the complexity of the data being rendered. It can be used to accelerate both time-coherent and noncoherent animation sequences.

An accurate segmentation is critical for clinical application of medical images. The undesirable partial-volume-effect, which lies on a boundary between a high intensity region and a low intensity region, makes unerring boundary determination a difficult task. A new approach to segmentation is required for removing the adverse effect on the boundary, which is unwanted especially from the point of view of volume rendering. Here, the authors propose a gradient magnitude based region growing algorithm for accurate segmentation. The gradient is useful for enhancing the boundary because it emphasizes the difference among voxel values. By analyzing the gradient magnitude, the authors can see the sufficient contrast which must be presented on the boundary region and they use this contrast to increase the accuracy of their segmentation method. The authors pay attention only to the boundary region, not to the whole large volumetric dataset itself, making it more computationally efficient.

We propose a novel approach for segmentation and digital cleansing of endoscopic organs. Our method can be used for a variety of segmentation needs with little or no modification. It aims at fulfilling the dual and often conflicting requirements of a fast and accurate segmentation and also eliminates the undesirable partial volume effect which contemporary approaches cannot. For segmentation and digital cleansing, we use the peculiar characteristics exhibited by the intersection of any two distinct-intensity regions. To detect these intersections we cast rays through the volume, which we call the segmentation rays as they assist in the segmentation. We then associate a certain task of reconstruction and classification with each intersection the ray detects. We further use volumetric contrast enhancement to reconstruct surface lost by segmentation (if any), which aids in improving the quality of the volume rendering.