Abstract: Method for mapping the transverse relaxation times (T2) in a magnetic resonance imaging (MRI) scan defined over a plurality of pixels, where each pixel is associated with a multicomponent T2 (mcT2) signal, comprises: accessing a computer readable medium storing an mcT2 dictionary having a set of synthetic mcT2 signals, and selecting a subset of synthetic mcT2 signals for which correlations between the synthetic mcT2 signals and pixels in the MRI scan are highest among the set. For each of at least a portion of the pixels, a respective mcT2 scan signal is fitted to the subset to provide, a plurality of T2 values for the pixel. A T2 map of the MRI scan is generated based on the T2 values.

Abstract: Method for mapping the transverse relaxation times (T2) in a magnetic resonance imaging (MRI) scan defined over a plurality of pixels, where each pixel is associated with a multicomponent T2 (mcT2) signal, comprises: accessing a computer readable medium storing an mcT2 dictionary having a set of synthetic mcT2 signals, and selecting a subset of synthetic mcT2 signals for which correlations between the synthetic mcT2 signals and pixels in the MRI scan are highest among the set. For each of at least a portion of the pixels, a respective mcT2 scan signal is fitted to the subset to provide, a plurality of T2 values for the pixel. A T2 map of the MRI scan is generated based on the T2 values.

Abstract: Method, apparatus and computer program product that uses a novel algorithm for edge detection suitable for both natural as well as noisy images. A scale adaptive threshold is used along with a recursive decision process to reveal the significant edges of all lengths and orientations and to localize them accurately even in low-contrast and very noisy images. Further the algorithm is use for fiber detection and enhancement by utilizing stochastic completion-like process from both sides of a fiber. The algorithm relies on an efficient multiscale algorithm for computing all “significantly different” oriented means in an image in 0(N log p), where N is the number of pixels in the image, and p is the length of the longest structure of interest. Experimental results on both natural and noisy images present confirmation of the method, apparatus and computer program product.

Abstract: A method and apparatus for finding correspondence between portions of two images that first subjects the two images to segmentation by weighted aggregation (10), then constructs directed acylic graphs (16,18) from the output of the segmentation by weighted aggregation to obtain hierarchical graphs of aggregates (20,22), and finally applies a maximally weighted subgraph isomorphism to the hierarchical graphs of aggregates to find matches between them (24). Two algorithms are described; one seeks a one-to-one matching between regions, and the other computes a soft matching, in which is an aggregate may have more than one corresponding aggregate. A method and apparatus for image segmentation based on motion cues. Motion provides a strong cue for segmentation. The method begins with local, ambiguous optical flow measurements. It uses a process of aggregation to resolve the ambiguities and reach reliable estimates of the motion.

Abstract: A novel multiscale approach that combines segmentation with classification to detect abnormal brain structures in medical imagery, and demonstrate its utility in detecting multiple sclerosis lesions in 3D MRI data. The method uses segmentation to obtain a hierarchical decomposition of a multi-channel, anisotropic MRI scan. It then produces a rich set of features describing the segments in terms of intensity, shape, location, and neighborhood relations. These features are then fed into a decision tree-based classifier, trained with data labeled by experts, enabling the detection of lesions in all scales. Unlike common approaches that use voxel-by-voxel analysis, our system can utilize regional properties that are often important for characterizing abnormal brain structures. Experiments show successful detections of lesions in both simulated and real MR images.

Abstract: Method, apparatus and computer program product that uses a novel algorithm for edge detection suitable for both natural as well as noisy images. A scale adaptive threshold is used along with a recursive decision process to reveal the significant edges of all lengths and orientations and to localize them accurately even in low-contrast and very noisy images. Further the algorithm is use for fiber detection and enhancement by utilizing stochastic completion-like process from both sides of a fiber. The algorithm relies on an efficient multiscale algorithm for computing all “significantly different” oriented means in an image in 0(N log p), where N is the number of pixels in the image, and p is the length of the longest structure of interest. Experimental results on both natural and noisy images present confirmation of the method, apparatus and computer program product.

Abstract: A method and apparatus for finding correspondence between portions of two images that first subjects the two images to segmentation by weighted aggregation (10), then constructs directed acylic graphs (16,18) from the output of the segmentation by weighted aggregation to obtain hierarchical graphs of aggregates (20,22), and finally applies a maximally weighted subgraph isomorphism to the hierarchical graphs of aggregates to find matches between them (24). Two algorithms are described; one seeks a one-to-one matching between regions, and the other computes a soft matching, in which is an aggregate may have more than one corresponding aggregate. A method and apparatus for image segmentation based on motion cues. Motion provides a strong cue for segmentation. The method begins with local, ambiguous optical flow measurements. It uses a process of aggregation to resolve the ambiguities and reach reliable estimates of the motion.