Compression of medical images using enhanced vector quantizer designed with self organizing feature maps

Authors:
Yogesh H. Dandawate;Madhuri A. Joshi;Shrirang Umrani
Affiliations:
Department of Electronics and Telecommunications, Vishwakarma Institute of Information Technology, Pune;Department of Electronics and Telecommunications, College of Engineering, Pune;Vishwakarma Institute of Information Technology, Pune
Venue:
ICMB'08 Proceedings of the 1st international conference on Medical biometrics
Year:
2008

Citing 3
Cited 0

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Abstract

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.