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This paper suggests a novel image compression scheme, using the discrete wavelet transformation (DWT) and the k-means clustering technique, suitable for medical images, based on preservation of important second order correlation ("textural") features of either DWT coefficients or image pixel intensities. Moreover it suggests a novel reconstruction scheme based on Bayesian formalism. While rival image compression methodologies utilizing the DWT apply it to the whole original image uniformly, the herein presented novel approaches involve a more sophisticated scheme. That is, different compression ratios are applied to the wavelet coefficients belonging in the different regions of interest, in which either each wavelet domain band of the transformed image or the image itself is clustered, respectively, employing textural descriptors as criteria. These descriptors include cooccurrence matrices based measures. Regarding the first method, its reconstruction process involves using the inverse DWT on the remaining wavelet coefficients. Concerning the second method, its reconstruction process involves linear combination of the reconstructed regions of interest. Moreover, another more efficient variant of these reconstruction approaches is proposed, which reduces blocking effects and is based on Bayesian formalism. An experimental study is conducted to qualitatively assessing all approaches in comparison with the original DWT compression technique, when applied to a set of medical images acquired from endoscopic video sequences.
Date of Conference: 3-5 Oct. 2007