Wavelet based medical image compression with adaptive prediction

A lossless wavelet-based image compression method with adaptive prediction is proposed. Firstly, we analyze the correlations between wavelet coefficients to identify a proper wavelet basis function, then predictor variables are statistically test to determine which relative wavelet coefficients should be included in the prediction model. At last, prediction differences are encoded by an adaptive arithmetic encoder. Instead of relying on a fixed number of predictors on fixed locations, we proposed the adaptive prediction approach to overcome the multicollinearity problem. The proposed innovative approach integrating correlation analysis for selecting wavelet basis function with predictor variable selection is fully achieving high accuracy of prediction. Experimental results show that the proposed approach indeed achieves a higher compression rate on CT, MRI and ultrasound images comparing with several state-of-the-art methods.

Medical images are a special category of images in their characteristics and purposes. Medical images are generally acquired from special equipments, such as computed tomography (CT), magnetic resonance (MRI), ultrasound (US), X-ray diffraction, electrocardiogram (ECG), and positron emission tomography (PET). In practice, the compression of medical images must be lossless because a minor loss may result in a serious consequence. We here accordingly focus on the development of an adaptive prediction scheme for lossless medical image compression. One of the key techniques for efficient compression is prediction. The function of a prediction is to infer the current data by means of the previously known data. The predicted value should approximate the original value; in other words, the differences between the original data and the predicted values are expectedly minimal. In general, the compression efficiency is highly related to the accuracy of the prediction scheme [1]; thus a high accuracy prediction scheme is pursued. Many advanced image compression techniques have been developed in response to the increasing demands for medical images. JPEG2000 [2–4] combines embedded block coding

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