Content-Based Image Retrieval of 3D Cardiac Models to Aid the Diagnosis of Congestive Heart Failure by Using Spectral Clustering

This paper describes a novel application of Content-Based Image Retrieval (CBIR) to search a medical database consisting of 3D models for diagnosis purposes. The 3D models, which are generated using Magnetic Resonance Imaging and include depth information, are used to search for similarity a database of 3D annotated medical cases using their pairwise feature similarity. The 3D models consist of both local and global feature descriptors that consider the surface of the 3D model and the overall geometry of the medical artifact. The models are then matched using spectral clustering that embeds the Euclidean distance for affinity and partitions the models into two groups, Congestive Heart Failure (CHF) and non-CHF. This suffices to demarcate using pairwise similarity the existence of CHF for the left ventricle. Experimental results using thirty 3D models show the utility of the new 3D method compared to existing methods. In particular, the novel method yields 83% overall accuracy.