A fully automatic three-step liver segmentation method on LDA-based probability maps for multiple contrast MR images.

TL;DR: A systematic literature review concerning 3D segmentation algorithms for computerized tomographic imaging found in four scientific databases is presented, using the methodology for systematic review proposed by Kitchenham.

TL;DR: An Interactive Visual Analysis approach that enables epidemiologists to rapidly investigate the entire data pool for hypothesis validation and generation is proposed and demonstrated by validating and generating hypotheses related to lower back pain as a qualitative evaluation.

TL;DR: In this article, a method for providing augmented reality in minimally invasive surgery includes capturing pre-operative image data of internal organs of a patient, tracking the position and orientation of the endoscope during the surgical procedure, and augmenting the intra-operator image data captured by the end-oscope in real time with a rendering of at least a portion of an internal organ of the patient that is in registration with the real-time intra-operative image data from the endoscopy but outside of the field of view of an endoscope.

TL;DR: This study presents a fully automatic liver segmentation method by using an approximation of the level set based contour evolution from T2 weighted magnetic resonance data sets and shows that the automatic segmentation approach gives successful results.

TL;DR: A new model for active contours to detect objects in a given image, based on techniques of curve evolution, Mumford-Shah (1989) functional for segmentation and level sets is proposed, which can detect objects whose boundaries are not necessarily defined by the gradient.

TL;DR: A novel scheme for the detection of object boundaries based on active contours evolving in time according to intrinsic geometric measures of the image, allowing stable boundary detection when their gradients suffer from large variations, including gaps.

TL;DR: In this article, the authors show that when the training data set is small, PCA can outperform LDA and, also, that PCA is less sensitive to different training data sets.

TL;DR: Simple modification of a spin echo imaging pulse sequence generates useful spectroscopic information at 0.35 T and provides new parameters for tissue characterization and improved contrast between some organs.