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- W2334107430 abstract "DOI: 10.1049/ic.2009.0120 ISBN: 978-1-84919-193-7 Location: Maharashtra, India Conference date: 19-20 Sept. 2009 Format: PDF This paper presents a novel idea of designing codebook named as Generic codebook and Generic codebook with error for compression of medical images, which can be extended to the compression of color images and video frames. The codevectors are generated by evaluating the characteristics of the specific image sub samples, which are determined through rigorous mathematical operations and training the selected samples by Kohonen's Self Organizing Feature Map (SOFM) artificial neural network with adjustable learning rate and initializations conditions. The testing of the codebook is done with variety of medical images such as X-ray, CT, MRI etc and the compression performance is evaluated by using objective and subjective quality measures. Peak Signal to Noise Ratio (PSNR) is widely used measure for determining the quality ,which is deceptive many times, other quality measures such as Image fidelity , Structural content, Mean Structural Similarity Index (HVS based) Spatial frequency measure (SFM) and spectral activity measure (SAM) which deal with spectral activity and coding complexity are also used along with PSNR. Another novel concept of codebook design, which is Generic codebook with error is introduced, which shows extremely good performance. The texture analysis is also performed for Generic and Generic with error VQ's in order to evaluate the loss in texture. The quality is also determined using no reference quality measure. The proposed design is such, that it can be implemented with less complexity and cost using VLSI techniques in dedicated imaging devices. (5 pages) Inspec keywords: data compression; self-organising feature maps; image coding; medical image processing Subjects: Biomedical measurement and imaging; Image and video coding; Neural computing techniques; Codes; Medical and biomedical uses of fields, radiations, and radioactivity; health physics; Biology and medical computing" @default.
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- W2334107430 date "2009-01-01" @default.
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- W2334107430 title "Compression of medical images using generic vector quantizer designed with Kohonen's Self Organizing Feature Maps" @default.
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