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Compression of Medical Images using Improved Kohonen Algorithm

IJCA Special Issue on Software Engineering, Databases and Expert Systems
© 2012 by IJCA Journal
SEDEX - Number 1
Year of Publication: 2012
Mohamed Ettaouil
Mohamed Lazaar

Mohamed Ettaouil and Mohamed Lazaar. Article: Compression of Medical Images using Improved Kohonen Algorithm. IJCA Special Issue on Software Engineering, Databases and Expert Systems SEDEX(1):41-45, September 2012. Full text available. BibTeX

	author = {Mohamed Ettaouil and Mohamed Lazaar},
	title = {Article: Compression of Medical Images using Improved Kohonen Algorithm},
	journal = {IJCA Special Issue on Software Engineering, Databases and Expert Systems},
	year = {2012},
	volume = {SEDEX},
	number = {1},
	pages = {41-45},
	month = {September},
	note = {Full text available}


Nowadays, neural networks are largely used in signal processing and images. In particular, Kohonen networks or Self Organizing Maps are unsupervised learning models. This method performs a vector quantization (VQ) on the values obtained after processing. The vector quantization has a potential to give more data compression maintaining the same quality. In this paper we propose new scheme to image compression using Kohonen networks. The main innovation is to use the optimal Kohonen topological map to determine the optimal codebook, which can reduce the storage space, simplify data transfer and accelerate the process of data compression, unlike in classical Kohonen approach. To test our approach, we use the medical images. The results demonstrated the effectiveness of the proposed approach.


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