Now a days all medical imaging equipments give output as digital image and non-invasive techniques are becoming cheaper, the database of images is becoming larger. This archive of images increases up to significant size and in telemedicine-based applications the storage and transmission requires large memory and bandwidth respectively. There is a need for compression to save memory space and fast transmission over internet and 3G mobile with good quality decompressed image, even though compression is lossy. This paper presents a novel approach for designing enhanced vector quantizer, which uses Kohonen's Self Organizing neural network. The vector quantizer (codebook) is designed by training with a neatly designed training image and by selective training approach .Compressing; images using it gives better quality. The quality analysis of decompressed images is evaluated by using various quality measures along with conventionally used PSNR. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Dandawate, Y. H., Joshi, M. A., & Umrani, S. (2008). Compression of medical images using enhanced vector quantizer designed with self organizing feature maps. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4901 LNCS, pp. 256–264). https://doi.org/10.1007/978-3-540-77413-6_33
Mendeley helps you to discover research relevant for your work.