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.
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Dandawate, Y.H., Joshi, M.A., Umrani, S. (2007). Compression of Medical Images Using Enhanced Vector Quantizer Designed with Self Organizing Feature Maps. In: Zhang, D. (eds) Medical Biometrics. ICMB 2008. Lecture Notes in Computer Science, vol 4901. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77413-6_33
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DOI: https://doi.org/10.1007/978-3-540-77413-6_33
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