An Image Enhancement Algorithm Based on a Contrast Measure in the Wavelet Domain for Screening Mammograms

Currently, radiologists mainly use their eyes to discern cancer when they screen the mammograms. However, in many cases, cancer is not easily detected by the eyes because of bad imaging conditions. In order to improve the diagnostic rate of cancer, image enhancement technology is often used to enhance the image and aid the radiologists. In this paper, we developed a new image enhancement technology in the wavelet domain for radiologists to screen mammograms. The new image enhancement algorithm has several advantages. First, the image enhancement is based on a contrast measure defined in the wavelet domain which matches the human vision system better. The enhanced images are therefore more suitable for the human eye; second, the image enhancement is carried on in the wavelet domain which saves time if the image is compressed by JPEG2000. The algorithm was tested by an expert and the results are progressive.

[1]  Rangaraj M. Rangayyan,et al.  Region-based contrast enhancement of mammograms , 1992, IEEE Trans. Medical Imaging.

[2]  Jacob Scharcanski,et al.  Denoising and enhancing digital mammographic images for visual screening , 2006, Comput. Medical Imaging Graph..

[3]  Eli Peli,et al.  Image enhancement in the JPEG domain for people with vision impairment , 2004, IEEE Transactions on Biomedical Engineering.

[4]  L. Costaridou,et al.  A wavelet-based spatially adaptive method for mammographic contrast enhancement. , 2003, Physics in medicine and biology.

[5]  Hyun Wook Park,et al.  Adaptive Mammographic Image Enhancement Using First Derivative and Local Statistics , 1997, IEEE Trans. Medical Imaging.

[6]  Robin N. Strickland.  Image-Processing Techniques for Tumor Detection , 2007 .

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

[8]  Jian Fan,et al.  Mammographic feature enhancement by multiscale analysis , 1994, IEEE Trans. Medical Imaging.

[9]  Johann Drexl,et al.  Integrated wavelets for enhancement of microcalcifications in digital mammography , 2003, IEEE Transactions on Medical Imaging.

[10]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Jianmin Jiang,et al.  Integration of fuzzy logic and structure tensor towards mammogram contrast enhancement. , 2005, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[12]  Azeddine Beghdadi,et al.  Contrast enhancement technique based on local detection of edges , 1989, Comput. Vis. Graph. Image Process..

[13]  Alexander Toet,et al.  Multiscale contrast enhancement with applications to image fusion , 1992 .

[14]  G..  A Theory for Multiresolution Signal Decomposition : The Wavelet Representation , 2004 .

[15]  S. Acton,et al.  Image enhancement using a contrast measure in the compressed domain , 2003, IEEE Signal Processing Letters.

[16]  A P Dhawan,et al.  Mammographic feature enhancement by computerized image processing. , 1988, Computer methods and programs in biomedicine.

[17]  E. Peli.  Contrast in complex images. , 1990, Journal of the Optical Society of America. A, Optics and image science.

[18]  Andrew F. Laine,et al.  Evaluation of a multiscale enhancement protocol for digital mammography , 2000, SPIE Optics + Photonics.