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Irreversible Compression of MRIImages using a Hybrid ArtificialNeural Network Approach
KTH, School of Computer Science and Communication (CSC).
KTH, School of Computer Science and Communication (CSC).
2016 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
Irreversibel komprimering av MRI-bilder med artificiella neuronnätverk som del i tillvägagångssättet (Swedish)
Abstract [en]

Medical Imagery (MI) has made much progress in the lasttwo decades. Making use of new modalities and improvedaccess to technologies, such as magnetic resonance imaging(MRI), has lead to an increasing amount of images andrelated medical data. The increase in data calls for bettercompression methods to support fast communication andstorage of medical images.

In MI the most used compression techniques are JPEGand the more modern JPEG-2000 (JP2). Recent attemptsto improve compression has made use of artificial neuralnetworks (ANN) as part of one or more steps in the compressionprocess.

This report tries to compress MRI images using a hybridANN approach mostly based on the JP2 approach.The results were validated and compared using both MSEand PSNR as well as compression ratio. A dataset of 500MRI images from one patient was used in training and testingthe implementation. The results of this study were notcomparable to previous work and it fails to even come closeto the JP2 compression rate. This could largely be due toflaws in the implementation or not enough training of theANN, meaning that the proposed method could still be aviable approach for future research.

Place, publisher, year, edition, pages
National Category
Computer Science
URN: urn:nbn:se:kth:diva-186858OAI: diva2:928014
Available from: 2016-05-18 Created: 2016-05-13 Last updated: 2016-05-30Bibliographically approved

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