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5 April 2000 Diagnostically lossless medical image compression via wavelet-based background noise removal
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Abstract
Diagnostically lossless compression techniques are essential in archival and communication of medical images. In this paper, an automated wavelet-based background noise removal method, i.e. diagnostically lossless compression method, is proposed. First, the wavelet transform modulus maxima procedure products the modulus maxima image which contains sharp changes in intensity that are used to locate the edges of the images. Then the Graham Scan algorithm is used to determine the convex hull of the wavelet modulus maxima image and extract the foreground of the image, which contains the entire diagnostic region of the image. Histogram analyses are applied to the non-diagnostic region, which is approximated by the image that is outside the convex hull. After setting all pixels in the non-diagnostic region to zero intensity, a higher compression ratio, without introducing loss of any data used for the diagnosis, is achieved with UNIX utilities compress and pack, and with lossless JPEG. Furthermore, an image of smaller rectangular region containing all the diagnostic region is constructed to further improve the compression ratio achieved.
© (2000) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Xiaojun Qi, John M. Tyler, and Oleg S. Pianykh "Diagnostically lossless medical image compression via wavelet-based background noise removal", Proc. SPIE 4056, Wavelet Applications VII, (5 April 2000); https://doi.org/10.1117/12.381707
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