Mammography Image Enhancement using Linear, Nonlinear and Wavelet Filters with Histogram Equalization

Aziz Makandar, Bhagirathi Halalli

Abstract


In the worldwide, breast cancer is one of the major diseases among the women. In the modern medical science, there are plenty of newly devised methodologies and techniques for the timely detection of breast cancer. However, there are difficulties still exist for detecting breast cancer at an early stage for its diagnoses because of poor visualization and artifacts present in the mammography. Thus the Digital mammographic image preprocessing often requires, enhancement of the image to improve the quality while preserving important details. The proposed method works in three stages. First it removes all the artifacts present in the image. Second it denoise the image by using Linear, nonlinear and wavelet filters. Third, contrast of the image increased by histogram equalization. This method definitely helps to computer aided diagnosis system to increase the accuracy. The experimental results are tested on two standard datasets MIAS and DDSM. 


Keywords


Breast Cancer; Mammography; Medical Image Processing; Wavelet filters; Contrast Enhancement.

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References


Torre, L. A., Bray, F., Siegel, R. L., Ferlay, J., Lortet‐Tieulent, J., & Jemal, A.. “Global cancer statistics, 2012”. CA: a cancer journal for clinicians, 65(2), 87-108.

Bleyer, Archie, and H. Gilbert Welch. "Effect of three decades of screening mammography on breast-cancer incidence." New England Journal of Medicine 367.21 (2012): 1998-2005.

Suganthi, Muthusamy, and Muthusamy Madheswaran. "An improved medical decision support system to identify the breast cancer using mammogram." Journal of medical systems 36.1 (2012): 79-91.

Dheeba, J., and S. Tamil Selvi. "An improved decision support system for detection of lesions in mammograms using differential evolution optimized wavelet neural network." Journal of medical systems 36.5 (2012): 3223-3232.

Anuradha, K., and K. Sankaranarayanan. "Identification of suspicious regions to detect Oral cancers at an earlier stage—a literature survey." International Journal of Advances in Engineering & Technology 3.1 (2012): 84-91.

Subashini, T. S., Vennila Ramalingam, and S. Palanivel. "Automated assessment of breast tissue density in digital mammograms." Computer Vision and Image Understanding 114.1 (2010): 33-43.

Rahmati, Peyman, et al. "A new preprocessing filter for digital mammograms." International Conference on Image and Signal Processing. Springer Berlin Heidelberg, 2010.

Dehghani, Sara, and Mashallah Abbasi Dezfooli. "A method for improve preprocessing images mammography." International Journal of Information and Education Technology 1.1 (2011): 90.

Maitra, Indra Kanta, Sanjay Nag, and Samir Kumar Bandyopadhyay. "Technique for preprocessing of digital mammogram." Computer methods and programs in biomedicine 107.2 (2012): 175-188.

Ramani, R., N. Suthanthira Vanitha, and S. Valarmathy. "The pre-processing techniques for breast cancer detection in mammography images." International Journal of Image, Graphics and Signal Processing 5.5 (2013): 47.

Ramani, R., N. Suthanthira Vanitha, and S. Valarmathy. "The pre-processing techniques for breast cancer detection in mammography images." International Journal of Image, Graphics and Signal Processing 5.5 (2013): 47.

Zhang, Xinsheng, and Hua Xie. "Mammograms Enhancement and Denoising Using Generalized Gaussian Mixture Model in Nonsubsampled Contourlet Transform." Journal of Multimedia 4.6 (2009): 389-396.

Mayo, P., F. Rodenas, and G. Verdu. "Comparing methods to denoise mammographic images." Engineering in Medicine and Biology Society, 2004. IEMBS'04. 26th Annual International Conference of the IEEE. Vol. 1. IEEE, 2004.

Patel, Vishnukumar K., Syed Uvaid, and A. C. Suthar. "Mammogram of breast cancer detection based using image enhancement algorithm." Int. J. Emerg. Technol. Adv. Eng 2.2012 (2012): 143-147.

Mohideen, Kother, et al. "Image Denoising And Enhancement Using Multiwavelet With Hard Threshold In Digital Mammographic Images." Int. Arab J. e-Technol. 2.1 (2011): 49-55.

Sandhu, Tajinder Kaur Manjit, Preeti Goel, and Harpreet Singh. "Image Denoising using Multiscale Ridgelet for application on Mammographic image." International Journal of Engineering Research and Applications (IJERA)Vol. 1, Issue 3, pp.537-541

Suckling, John, et al. "The mammographic image analysis society digital mammogram database." Exerpta Medica. International Congress Series. Vol. 1069. 1994.

The Digital Database for Screening Mammography (DDSM) http://marathon.csee.usf.edu/Mammography/Database.html accessed on 05-02-2017.

Wu, Zhe, et al. "Digital mammography image enhancement using improved unsharp masking approach." Image and Signal Processing (CISP), 2010 3rd International Congress on. Vol. 2. IEEE, 2010.

Makandar, Aziz, and Bhagirathi Halalli. "Breast cancer image enhancement using median filter and clahe." International Journal of Scientific & Engineering Research 6.4 (2015): 462-465.

Sundaram, M., et al. "Histogram modified local contrast enhancement for mammogram images." Applied soft computing 11.8 (2011): 5809-5816.

Rabbani, Hossein, Reza Nezafat, and Saeed Gazor. "Wavelet-domain medical image denoising using bivariate laplacian mixture model." IEEE transactions on biomedical engineering 56.12 (2009): 2826-2837.

Morlet, D., et al. "Time-scale analysis of high-resolution signal-averaged surface ECG using wavelet transformation." Computers in Cardiology 1991, Proceedings.. IEEE, 1991.

Mallat, Stephane. "Wavelets for a vision." Proceedings of the IEEE 84.4 (1996): 604-614.

Torrents-Barrena, Jordina, et al. "Computer-aided diagnosis of breast cancer via Gabor wavelet bank and binary-class SVM in mammographic images." Journal of Experimental & Theoretical Artificial Intelligence 28.1-2 (2016): 295-311.

Harikumar, R. "Performance analysis of neural networks for classification of medical images with wavelets as a feature extractor." International Journal of Imaging Systems and Technology 25.1 (2015): 33-40.

Reza, Ali M. "Realization of the contrast limited adaptive histogram equalization (CLAHE) for real-time image enhancement." The Journal of VLSI Signal Processing 38.1 (2004): 35-44.

Pizer, Stephen M., et al. "Adaptive histogram equalization and its variations." Computer vision, graphics, and image processing 39.3 (1987): 355-368.

Prashanth, H. S., H. L. Shashidhara, and Balasubramanya Murthy KN. "Image scaling comparison using universal image quality index." Advances in Computing, Control, & Telecommunication Technologies, 2009. ACT'09. International Conference on. IEEE, 2009.


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