Applying multiresolution methods to medical image enhancement

Each image acquired from a medical imaging system is often part of a two-dimensional (2-D) image set whose total presents a three-dimensional (3-D) object for diagnosis. Unfortunately, sometimes these images are of poor quality. These distortions cause an inadequate object-of-interest presentation, which can result in inaccurate image analysis. Blurring is considered a serious problem. Therefore, "deblurring" an image to obtain better quality is an important issue in medical image processing.In our research, the image is initially decomposed. Contrast improvement is achieved by modifying the coefficients obtained from the decomposed image. Small coefficient values represent subtle details and are amplified to improve the visibility of the corresponding details. The stronger image density variations make a major contribution to the overall dynamic range, and have large coefficient values. These values can be reduced without much information loss.

[1]  Andrew F. Laine,et al.  Wavelets for contrast enhancement of digital mammography , 1995 .

[2]  Fionn Murtagh,et al.  Gray and color image contrast enhancement by the curvelet transform , 2003, IEEE Trans. Image Process..

[3]  F. Campbell,et al.  Contrast and spatial frequency. , 1974, Scientific American.

[4]  Baoyu Zheng,et al.  Computer assisted diagnosis for digital mammography , 1995 .

[5]  Emile Schoeters,et al.  Image Processing in Computed Radiography , 1999 .

[6]  Giovanni Ramponi,et al.  Image enhancement via adaptive unsharp masking , 2000, IEEE Trans. Image Process..

[7]  Chulhee Lee,et al.  Registration and statistical analysis of PET images using the wavelet transform , 1995 .

[8]  Zhou Wang,et al.  Local Phase Coherence and the Perception of Blur , 2003, NIPS.

[9]  Giovanni Ramponi,et al.  A cubic unsharp masking technique for contrast enhancement , 1998, Signal Process..

[10]  Edward H. Adelson,et al.  The Laplacian Pyramid as a Compact Image Code , 1983, IEEE Trans. Commun..

[11]  Sabine Dippel,et al.  Multiscale contrast enhancement for radiographies: Laplacian pyramid versus fast wavelet transform , 2002, IEEE Transactions on Medical Imaging.

[12]  Zhou Wang,et al.  Multi-scale structural similarity for image quality assessment , 2003 .

[13]  M I Sezn,et al.  Automatic anatomically selective image enhancement in digital chest radiography. , 1989, IEEE transactions on medical imaging.

[14]  Bruce Kuo Ting Ho,et al.  Applying wavelet transforms with arithmetic coding to radiological image compression , 1995 .

[15]  Guest Editorial: Wavelets in Medical Imaging - Medical Imaging, IEEE Transactions on , 2001 .

[16]  Sanjit K. Mitra,et al.  A new class of nonlinear filters for image enhancement , 1991, [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing.

[17]  Sanjit K. Mitra,et al.  Nonlinear unsharp masking methods for image contrast enhancement , 1996, J. Electronic Imaging.

[18]  Pieter Vuylsteke,et al.  Multiscale image contrast amplification (MUSICA) , 1994, Medical Imaging.

[19]  A.N. Netravali,et al.  Picture coding: A review , 1980, Proceedings of the IEEE.

[20]  P. J. Burt,et al.  Fast Filter Transforms for Image Processing , 1981 .

[21]  L D Cromwell,et al.  Filtering noise from images with wavelet transforms , 1991, Magnetic resonance in medicine.