Medical image compression and characterization using vector quantization: an application of self-organizing maps and quadtree decomposition

Vector quantization (VQ) is an effective image compression approach. Among the different existing algorithms, Kohonen's self organizing feature map (SOFM) is one of the well known methods for VQ. It allows efficient codebook design with interesting topological properties. Furthermore, use of VQ for compression gives, in the same process, basic information on the image content. But in order to preserve the diagnostic accuracy in medical applications, the block size is restricted to small values (3×3, 4×4), which limits the compression rate. We propose to improve the compression performance by using several codebooks containing codewords of different size, according to the quadtree decomposition of the images. Results are compared to those provided by the standard JPEG image compression algorithm. Finally we explain how it is possible to generate characteristic signature maps of images using compression information. The paper represents an extension of the work presented by G. Cauguel et al. (1997)