Medical image enhancement based on nonlinear technique and logarithmic transform coefficient histogram matching

This paper presents a new method of medical image enhancement that improves the visual quality of digital images as well as images that exhibits dark shadows due to limited dynamic range of imaging. In this paper, non linear image enhancement technique is used in transform domain by the way of transform coefficient histogram matching to enhance image. Processing includes global dynamic range correction and local contrast enhancement which is able to enhance the luminance in the dark shadows keeping the overall tonality consistent with that of the input image. Logarithmic transform histogram matching is used which uses the fact that the relation between stimulus and perception is logarithmic. A measure of enhancement based on the transform is used as a tool for evaluating the performance contrast measure with respect of the proposed enhancement technique. The performance of the algorithm is compared quantitatively to classical histogram equalization using the aforementioned measure of enhancement. A number of experimental results over some x-ray and facial images are presented to show the performance of the proposed algorithm alongside classical histogram equalization.

[1]  Guoping Qiu,et al.  Novel histogram processing for colour image enhancement , 2004, Third International Conference on Image and Graphics (ICIG'04).

[2]  K. E. Prager,et al.  Image enhancement and filtering using wavelets , 1991, [1991] Conference Record of the Twenty-Fifth Asilomar Conference on Signals, Systems & Computers.

[3]  Okan K. Ersoy,et al.  Transform image enhancement , 1992, Optical Society of America Annual Meeting.

[4]  Abd. Rahman Ramli,et al.  Minimum mean brightness error bi-histogram equalization in contrast enhancement , 2003, IEEE Trans. Consumer Electron..

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

[6]  J. McClellan Artifacts in alpha-rooting of images , 1980, ICASSP.

[7]  Malur K. Sundareshan,et al.  Adaptive image contrast enhancement based on human visual properties , 1994, IEEE Trans. Medical Imaging.

[8]  Sos S. Agaian,et al.  Transform Coefficient Histogram-Based Image Enhancement Algorithms Using Contrast Entropy , 2007, IEEE Transactions on Image Processing.

[9]  Vijayan K. Asari,et al.  Nonlinear Image Enhancement to Improve Face Detection in Complex Lighting Environment , 2006 .

[10]  John D. Austin,et al.  Adaptive histogram equalization and its variations , 1987 .

[11]  T H Lin,et al.  Adaptive local contrast enhancement method for medical images displayed on a video monitor. , 2000, Medical engineering & physics.

[12]  Stéphane Mallat,et al.  Characterization of Signals from Multiscale Edges , 2011, IEEE Trans. Pattern Anal. Mach. Intell..

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

[14]  B. Prasada,et al.  Adaptive quantization of picture signals using spatial masking , 1977, Proceedings of the IEEE.

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

[16]  Sos S. Agaian,et al.  Transform-based image enhancement algorithms with performance measure , 2001, IEEE Trans. Image Process..

[17]  Sos S. Agaian,et al.  A New Measure of Image Enhancement , 2000 .