Medical Image Indexing and Compression Based on Vector Quantization: Image Retrieval Efficiency Evaluation

This paper addresses the problem of efficient image retrieval from a compressed image database, using information derived from the compression process. Images in the database are compressed applying two approaches: Vector Quantization (VQ) and Quadtree image decomposition. Both are based on Konohen’s Self-Organizing Feature Maps (SOFM) for creating vector quantization codebooks. However, while VQ uses one codebook of one resolution to compress the images, Quadtree decomposition uses simultaneously 4 codebooks of four different resolutions. Image indexing is implemented by generating a Feature Vector (FV) for each compressed image. Accordingly, images are retrieved by means of FVs similary evaluation between the query image and the images in the database, depending on a distance measure. Three distance measures have been analyzed to assess FV index similarity: Euclidean, Intersection and Correlation distances. Distance measures efficiency retrieval is evaluated for different VQ resolutions and different Quadtree image descriptors. Experimental results using real data, esophageal ultrasound and eye angiography images, are presented.