Groupwise registration of a set of shapes represented by unlabeled point sets is a challenging problem since, usually, this involves solving for point correspondence in a nonrigid motion setting. In this paper, we propose a novel and robust algorithm that is capable of simultaneously computing the mean shape, represented by a probability density function, from multiple unlabeled point sets (represented by finite-mixture models), and registering them nonrigidly to this emerging mean shape. This algorithm avoids the correspondence problem by minimizing the Jensen-Shannon (JS) divergence between the point sets represented as finite mixtures of Gaussian densities. We motivate the use of the JS divergence by pointing out its close relationship to hypothesis testing. Essentially, minimizing the JS divergence is asymptotically equivalent to maximizing the likelihood ratio formed from a probability density of the pooled point sets and the product of the probability densities of the individual point sets. We derive the analytic gradient of the cost function, namely, the JS-divergence, in order to efficiently achieve the optimal solution. The cost function is fully symmetric, with no bias toward any of the given shapes to be registered and whose mean is being sought. A by-product of the registration process is a probabilistic atlas, which is defined as the convex combination of the probability densities of the input point sets being aligned. Our algorithm can be especially useful for creating atlases of various shapes present in images and for simultaneously (rigidly or nonrigidly) registering 3D range data sets (in vision and graphics applications), without having to establish any correspondence. We present experimental results on nonrigidly registering 2D and 3D real and synthetic data (point sets).

Heart auscultation and ECG are two very important and commonly used diagnostic aids in cardiovascular disease diagnosis. Physicians routinely perform diagnosis from simple heart auscultation and visual examination of ECG waveform shapes. It is common knowledge to physicians that patients with the same disease have similar-looking ECG shapes and comparable heart sounds. A key idea explored in this paper is to automatically capture such shape similarity in the ECG and audio signals, which are combined to find disease similarity. Specifically, we present a general method of capturing the perceptual shape similarity of the ECG and audio waveforms by modeling the morphological variations in the signals representing the same disease across patients. Differences in shape corresponding to the same disease are modeled as a constrained non-rigid translation. Patients with similar diseases are retrieved by recovering the non-rigid alignment transform using a variant of dynamic time warping. Results are presented that demonstrate the method on audio shape-based discrimination of various cardiovascular diseases.

Diagnostic decision support is still very much an art for physicians in their practices today due to lack of quantitative tools. AALIM is a decision support system for cardiology that exploits the consensus opinions of other physicians who have looked at similar patients, to present statistical reports summarizing possible diagnoses. The key idea behind our statistical decision support system is the search for similar patients based on the underlying multimodal data. In this paper, we describe the AALIM decision support system and the underlying multimodal similarity search used for cardiac data sets.

Flow Doppler imaging has become an integral part of an echocardiographic exam. Automated interpretation of flow doppler imaging has so far been restricted to obtaining hemodynamic information from velocity-time profiles depicted in these images. In this paper we exploit the shape patterns in Doppler images to infer the similarity in valvular disease labels for purposes of automated clinical decision support. Specifically, we model the similarity in appearance of Doppler images from the same disease class as a constrained non-rigid translation transform of the velocity envelopes embedded in these images. The shape similarity between two Doppler images is then judged by recovering the alignment transform using a variant of dynamic shape warping. Results of similarity retrieval of doppler images for cardiac decision support on a large database of images are presented.

Abstract. 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.