Tensor-product kernel-based representation encoding joint MRI view similarity.

To support 3D magnetic resonance image (MRI) analysis, a marginal image similarity (MIS) matrix holding MR inter-slice relationship along every axis view (Axial, Coronal, and Sagittal) can be estimated. However, mutual inference from MIS view information poses a difficult task since relationships between axes are nonlinear. To overcome this issue, we introduce a Tensor-Product Kernel-based Representation (TKR) that allows encoding brain structure patterns due to patient differences, gathering all MIS matrices into a single joint image similarity framework. The TKR training strategy is carried out into a low dimensional projected space to get less influence of voxel-derived noise. Obtained results for classifying the considered patient categories (gender and age) on real MRI database shows that the proposed TKR training approach outperforms the conventional voxel-wise sum of squared differences. The proposed approach may be useful to support MRI clustering and similarity inference tasks, which are required on template-based image segmentation and atlas construction.

Novel heuristic search for ventricular arrhythmia detection using normalized cut clustering.

Processing of the long-term ECG Holter recordings for accurate arrhythmia detection is a problem that has been addressed in several approaches. However, there is not an outright method for heartbeat classification able to handle problems such as the large amount of data and highly unbalanced classes. This work introduces a heuristic-search-based clustering to discriminate among ventricular cardiac arrhythmias in Holter recordings. The proposed method is posed under the normalized cut criterion, which iteratively seeks for the nodes to be grouped into the same cluster. Searching procedure is carried out in accordance to the introduced maximum similarity value. Since our approach is unsupervised, a procedure for setting the initial algorithm parameters is proposed by fixing the initial nodes using a kernel density estimator. Results are obtained from MIT/BIH arrhythmia database providing heartbeat labelling. As a result, proposed heuristic-search-based clustering shows an adequate performance, even in the presence of strong unbalanced classes.