A Structured Learning Approach for Medical Image Indexing and Retrieval
Joo‐Hwee Lim, Jean–Pierre Chevallet · 2005
Medical images are critical assets for medical diagnosis, research, and teaching. To facilitate automatic indexing and retrieval of large medical image databases, we propose a structured framework for designing and learning vocabularies of meaningful medical terms with associated visual appearance from image samples. These VisMed terms span a new feature space to represent medical image contents. After a multi-scale detection process, a medical image is indexed as compact spatial distributions of VisMed terms. When queries are in the form of example images, both a query image and a database image can be matched based on their distributions of VisMed terms, much like the matching of feature-based histograms though the bins refer to semantic medical terms. In addition, a flexible tiling (FlexiTile) matching scheme has been proposed to compare the similarity between two medical images of arbitrary aspect ratios. This matching scheme supports similarity-based retrieval with visual queries. The ranked list of such retrieval is denoted as “i2r-vk-sim.txt ” in our submission to ImageCLEF 2005.