Medical image compression based on region of interest, with application to colon CT images

CT or MRI medical imaging produce human body pictures in digital form. Since these imaging techniques produce prohibitive amounts of data, compression is necessary for storage and communication purposes. Many current compression schemes provide a very high compression rate but with considerable loss of quality. On the other hand, in some areas in medicine, it may be sufficient to maintain high image quality only in the region of interest, i.e., in diagnostically important regions. This paper discusses a hybrid model of lossless compression in the region of interest, with high-rate, motion-compensated, lossy compression in other regions. We evaluate our method on medical CT images, and show that it outperforms other common compression schemes, such as discrete cosine transform, vector quantization, and principal component analysis. In our experiments, we emphasize CT imaging of the human colon.

[1]  Jean Serra,et al.  Image Analysis and Mathematical Morphology , 1983 .

[2]  G.G. Langdon,et al.  Data compression , 1988, IEEE Potentials.

[3]  Gregory K. Wallace,et al.  The JPEG still picture compression standard , 1991, CACM.

[4]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[5]  Allen Gersho,et al.  Vector quantization and signal compression , 1991, The Kluwer international series in engineering and computer science.

[6]  Bernd Girod,et al.  Vector quantization for entropy coding of image subbands , 1992, IEEE Trans. Image Process..

[7]  James A. Storer,et al.  Data Compression , 1992, Inf. Process. Manag..

[8]  Hugues Benoit-Cattin,et al.  Coding of 3D medical images using 3D wavelet decompositions , 1993, 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[9]  Carlo Tomasi,et al.  Good features to track , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[10]  Pamela C. Cosman,et al.  Evaluating quality of compressed medical images: SNR, subjective rating, and diagnostic accuracy , 1994, Proc. IEEE.

[11]  P. Wingo,et al.  Cancer statistics, 1995 , 1995, CA: a cancer journal for clinicians.

[12]  R.M. Gray,et al.  Evaluating quality and utility in digital mammography , 1995, Proceedings., International Conference on Image Processing.

[13]  Aurel Vlaicu,et al.  New compression techniques for storage and transmission of 2D and 3D medical images , 1995, Other Conferences.

[14]  Li Yuan,et al.  Information preserved guided scan pixel difference coding for medical images , 1995, IEEE WESCANEX 95. Communications, Power, and Computing. Conference Proceedings.

[15]  Michifumi Yoshioka,et al.  Image compression by nonlinear principal component analysis , 1996, Proceedings 1996 IEEE Conference on Emerging Technologies and Factory Automation. ETFA '96.

[16]  Chin-Wang Tao,et al.  Medical image compression using principal component analysis , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[17]  Yao Wang,et al.  Multispectral code excited linear prediction coding and its application in magnetic resonance images , 1997, IEEE Trans. Image Process..

[18]  Carlo Tomasi,et al.  A graph method for the conservative detection of the polyps in the colon , 2000 .

[19]  Wilfried Philips,et al.  Exploiting interframe redundancies in the lossless compression of 3D medical images , 2000, Proceedings DCC 2000. Data Compression Conference.