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Compression techniques are vital for efficient storage and fast transfer of medical image data. The existing compression techniques take significant amount of time for performing encoding and decoding and hence the purpose of compression is not fully satisfied. In this paper a rapid 4-D lossy compression method constructed using data rearrangement, wavelet-based contourlet transformation and a modified binary array technique has been proposed for functional magnetic resonance imaging (fMRI) images. In the proposed method, the image slices of fMRI data are rearranged so that the redundant slices form a sequence. The image sequence is then divided into slices and transformed using wavelet-based contourlet transform (WBCT). In WBCT, the high frequency sub-band obtained from wavelet transform is further decomposed into multiple directional sub-bands by directional filter bank to obtain more directional information. The relationship between the coefficients has been changed in WBCT as it has more directions. The differences in parent–child relationships are handled by a repositioning algorithm. The repositioned coefficients are then subjected to quantization. The quantized coefficients are further compressed by modified binary array technique where the most frequently occurring value of a sequence is coded only once. The proposed method has been experimented with fMRI images the results indicated that the processing time of the proposed method is less compared to existing wavelet-based set partitioning in hierarchical trees and set partitioning embedded block coder (SPECK) compression schemes [1]. The proposed method could also yield a better compression performance compared to wavelet-based SPECK coder. The objective results showed that the proposed method could gain good compression ratio in maintaining a peak signal noise ratio value of above 70 for all the experimented sequences. The SSIM value is equal to 1 and the value of CC is greater than 0.9 for all experiments. Subjective evaluation on the reconstructed images indicated that the proposed method could reproduce the diagnostic features of fMRI images clearly.

In this study, the authors have proposed a new rapid method for the compression of coronary angiogram video sequences. The method is based on the philosophy that diagnostically significant areas of the image should be allocated the greatest proportion of the total allocated bit-budget. The approach uses a wavelet-based contourlet transform coder based on the set partitioned embedded block coder. Incorporated into this framework is a region-of-interest (ROI) detection technique. The combined result is an approach that removes the low-level contourlet coefficients for some diagnostically insignificant regions of the image in an extremely efficient manner. This allows additional bits to be used within the ROI to improve the quality of the significant areas. The results are compared for a number of real data sets and evaluated by trained cardiologists.

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