Comput. Biol. Med. - Medical ultrasound image compression using contextual vector quantization.

Tópicos

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Resumo

With ever increasing use of medical ultrasound (US) images, a challenge exists to deal with storage and transmission of these images while still maintaining high diagnostic quality. In this article, a state-of-the-art context based method is proposed to overcome this challenge called contextual vector quantization (CVQ). In this method, a contextual region is defined as a region containing the most important information and must be encoded without considerable quality loss. Attempts are made to encode this region with high priority and high resolution (low compression ratio and high bit rate) CVQ algorithm; and the background, which has a lower priority, is separately encoded with a low resolution (high compression ratio and low bit rate) version of the CVQ algorithm. Finally both of the encoded contextual region and the encoded background region is merged together to reconstruct the output image. As a result, very good diagnostic image quality with lower image size and enhanced performance parameters including mean square error (MSE), pick signal to noise ratio (PSNR) and coefficient of correlation (CoC) are gained. The experimental results show that the proposed CVQ methodology is superior as compared to other existing methods (general methods such as JPEG and JPEG2K, and ROI based methods such as EBCOT and CSPIHT) in terms of measured performance parameters. This makes CVQ compression method a feasible technique to overcome storage and transmission limitations.

Resumo Limpo

ever increas use medic ultrasound us imag challeng exist deal storag transmiss imag still maintain high diagnost qualiti articl stateoftheart context base method propos overcom challeng call contextu vector quantiz cvq method contextu region defin region contain import inform must encod without consider qualiti loss attempt made encod region high prioriti high resolut low compress ratio high bit rate cvq algorithm background lower prioriti separ encod low resolut high compress ratio low bit rate version cvq algorithm final encod contextu region encod background region merg togeth reconstruct output imag result good diagnost imag qualiti lower imag size enhanc perform paramet includ mean squar error mse pick signal nois ratio psnr coeffici correl coc gain experiment result show propos cvq methodolog superior compar exist method general method jpeg jpegk roi base method ebcot cspiht term measur perform paramet make cvq compress method feasibl techniqu overcom storag transmiss limit

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