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In clinical practice, physicians often exploit previously observed patterns in coronary angiograms from similar patients to quickly assess the state of the disease in a current patient. These assessments involve visually observed features such as the distance of a junction from the root and the tortuosity of the arteries. In this paper, we show how these visual features can be automatically extracted from coronary artery images and used for finding similar coronary angiograms from a database. Testing on a large collection has shown the method finds clinically similar coronary angiograms from patients with similar clinical history.

Automatic or semi-automatic segmentation and tracking of artery trees from computed tomography angiography (CTA) is an important step to improve the diagnosis and treatment of artery diseases, but it still remains a significant challenging problem. In this paper, we present an artery extraction method to address the challenge. The proposed method consists of two steps: (1) a geometric moments based tracking to secure a rough centerline, and (2) a fully automatic generalized cylinder structure-based snake method to refine the centerlines and estimate the radii of the arteries. In this method, a new line direction based on first and second order geometric moments is adopted while both gradient and intensity information are used in the snake model to improve the accuracy. The approach has been evaluated on synthetic images as well as 8 clinical coronary CTA images with 32 coronary arteries. Our method achieves 94.7% overlap tracking ability within an average distance inside the vessel of 0.36 mm.

In this paper, we propose a generalized group-wise non-rigid registration strategy for multiple unlabeled point-sets of unequal cardinality, with no bias toward any of the given point-sets. To quantify the divergence between the probability distributions--specifically Mixture of Gaussians--estimated from the given point sets, we use a recently developed information-theoretic measure called Jensen-Renyi (JR) divergence. We evaluate a closed-form JR divergence between multiple probabilistic representations for the general case where the mixture models differ in variance and the number of components. We derive the analytic gradient of the divergence measure with respect to the non-rigid registration parameters, and apply it to numerical optimization of the group-wise registration, leading to a computationally efficient and accurate algorithm. We validate our approach on synthetic data, and evaluate it on 3D cardiac shapes.

This paper proposes a novel and robust approach to the groupwise point-sets registration problem in the presence of large amounts of noise and outliers. Each of the point sets is represented by a mixture of Gaussians and the point-sets registration is treated as a problem of aligning the multiple mixtures. We develop a novel divergence measure which is defined between any arbitrary number of probability distributions based on L2 distance, and we call this new divergence measure "Generalized L2-divergence". We derive a closed-form expression for the Generalized-L2 divergence between multiple Gaussian mixtures, which in turn leads to a computationally efficient registration algorithm. This new algorithm has an intuitive interpretation, is simple to implement and exhibits inherent statistical robustness. Experimental results indicate that our algorithm achieves very good performance in terms of both robustness and accuracy.

Multi-fiber reconstruction has attracted immense attention lately in the field of diffusion weighted MRI analysis. Several mathematical models have been proposed in literature but there is still scope for improvement. The key issues of importance in multi-fiber reconstruction are, fiber detection accuracy, robustness to noise and computational efficiency. To this end, we propose a novel mathematical model for representing the MR signal attenuation in the presence of multiple fibers at a single voxel and estimate the parameters of this model given the diffusion weighted MRI data. Our model for the diffusion MR signal consists of a continuous mixture of Hyperspherical von Mises-Fisher distributions. Being a continuous mixture, our model does not require the specification of the number of mixture components. We present a closed form expression for this continuous mixture that leads to a computationally efficient implementation. To validate our model we present extensive results on both synthetic and real data (human and rat brain) and demonstrate that even in presence of noise, our model clearly outperforms the state-of-the-art methods in fiber orientation estimation while maintaining a substantial computational advantage.

Disease-specific understanding of echocardiographic sequences requires accurate characterization of spatio-temporal motion patterns. In this paper we present a method of automatic extraction and matching of spatio-temporal patterns from cardiac echo videos. Specifically, we extract cardiac regions (chambers and walls) using a variation of multiscale normalized cuts that combines motion estimates from deformable models with image intensity. We then derive spatio-temporal trajectories of region measurements such as wall motion, volume and thickness. The region trajectories are then matched to infer the similarities in disease labels of patients. Validation results on patient data sets collected from many hospitals are presented.

The functional state of an organism is determined largely by the pattern of expression of its genes. The analysis of gene expression data from gene chips has primarily revolved around clustering and classification of the data using machine learning techniques based on the intensity of expression alone with the time-varying pattern mostly ignored. In this paper, we present a pattern recognition-based approach to capturing similarity by finding salient changes in the time-varying expression patterns of genes. Such changes can give clues about important events, such as gene regulation by cell-cycle phases, or even signal the onset of a disease. Specifically, we observe that dissimilarity between time series is revealed by the sharp twists and bends produced in a higher-dimensional curve formed from the constituent signals. Scale-space analysis is used to detect the sharp twists and turns and their relative strength with respect to the component signals is estimated to form a shape similarity measure between time profiles. A clustering algorithm is presented to cluster gene profiles using the scale-space distance as a similarity metric. Multi-dimensional curves formed from time series within clusters are used as cluster prototypes or indexes to the gene expression database, and are used to retrieve the functionally similar genes to a query gene profile. Extensive comparison of clustering using scale-space distance in comparison to traditional Euclidean distance is presented on the yeast genome database.