Enhancement of mammogram images by using entropy improvement approach
- 94 Downloads
Breast cancer in women has been the most often diagnosed cancer. Digital mammogram becomes the most effective imaging method to detect breast cancer in early stage. In breast cancer screening, radiologists typically miss about 10–30% of tumors due to the speculated margins of lesions. Mammogram is a low contrast image whose quality needs to be improved for better interpretation. The performance of the validation of pre-processing methods for mammogram image enhancement is done by performance metrics such as peak signal to noise ratio (PSNR) and mean square error (MSE). Good filtering technique having higher PSNR and low MSE value. Experimental results on the digital database for screening mammography images shown that the non-local mean filter is better for mammogram image enhancement. Further we proposed mammogram images enhancement by entropy improvement method by considering non-local filtered images. These methodologies could add to the effective discovery of masses and micro calcifications in mammograms.
KeywordsMammogram DDSM MSE PSNR Breast cancer Entropy
Mammography is the important tool for screening to find the tumor by using X-rays to form a picture of the breast. Breast cancer has turned out to be one of the usual happening types of malignancy in female, particularly in growing countries. It represents around 30% of all sorts of malignancies in ladies of urban India . Breast cancer in India around 1 lakh cases/year, in the age group of 55–59 years. According to the International Agency for Research on Cancer, 27 million of new cases of this disease are expected before 2030. The survey of the National Cancer Institute shows that 1 in 8 women in the United States have the chance of getting breast cancer at a certain point in their lives . The American Cancer Society  prescribes that female over the age of forty are screened once a year for mammography. Mammography plays an important role to notice abnormalities in the breast.
It offers elaborated information regarding anatomy, morphology and also the pathologies of breast for diagnosis of breast cancer. Irregular shapes or speculated margins have a higher likelihood of being malignant and regular shapes have a probability of being benign. The dense breast tissue on the mammogram looks white or light gray. This will create mammograms tougher to interpret in younger ladies . Image enhancement is a crucial step in enlightening the quality of the picture for clear understanding and recognition. The enhancement processes are classified as spatial and frequency domain .
2 Image pre-processing techniques
Normally impulse (salt and pepper)  and quantum noises  will degrade the mammogram images. The quantum noise will be observed in the images while capturing, because of reduced count X-ray photons, and affects whole image pixels. In salt and pepper noise model one pixel is allocated either lowest or highest intensity value. Remaining pixels will have any value from allowed dynamic range. Image filtering finds applications in smoothing, sharpening, removing noise and edge detection. Pre-processing techniques like mean, median, wiener, wavelet denoising, non-local mean and power law transformation are analyzed in our work.
2.1 Mean filter
2.2 Median filter
2.3 Wiener filter (WF)
2.4 Wavelet denoising filter
Wavelet transform  has certain disadvantages such as the absence of clear edge information and lack of shift invariance.
2.5 Non-local mean
This method substitutes the local comparison of pixels with a non-local patch comparison . This filter will not make assumptions about the locality of the most significant pixels used to reduce the noise of the current pixel. This filter  considers the patterns around the pixels. The NL-mean filter not only compares the grey value at one point, but also the geometric structure in an entire neighborhood. Thus, this filter is more robust than other neighborhood filters .
2.6 Power law transformation
3 Proposed approach for entropy improvement
For enhancement of mammogram, various filter’s performance has been evaluated. As per the result analysis nonlocal mean filter performance is better. Hence, non-local mean filter has been considered in the proposed approach. In the flow diagram contrast enhancement is carried out with histogram equalization [21, 22] on the mammogram image data set. Then the morphological operation has been carried out for image smoothing. After that, non-local mean filter has been applied and entropy has been calculated. The improved entropy values shown in Table 2.
4 Experimental results
The statistical measurements will measure the enhancement of the image. The MSE, PSNR and entropy are used to analyze the performance of the pre-processing methods.
Here M * N is image size, p(X, Y) input image pixel value and δ(X, Y) estimated image pixel value.
4.2 Peak signal noise ratio
Here R represents the highest pixel value.
Comparison of different pre-processing techniques for the mammogram image
Non local means
Power law transformation (\(\gamma = 0.5)\)
Entropy improvement for the mammogram image
Input mammogram image
Preprocessed image with entropy improvement approach
Breast cancer research is highly required nowadays. Pre-processing techniques are essential for enhancing the low contrast mammogram image. Here performance of the pre-processing techniques is measured using performance metrics such as MSE and PSNR. The comparisons of techniques are verified for mammogram images. Among all the techniques have been experimented non-local mean filter is appropriate for the mammogram image noise reduction because it gives high PSNR and low MSE. Entropy improvement for mammogram images is observed in our proposed methodology, which improves average information content of the image. Further research is being conducted in this area to identify and classify the breast cancer in accordance with Breast Imaging Reporting and Data System (BIRADS).
Compliance with ethical standards
The authors declare that they have no conflict interest.
- 1.Trends of breast cancer in India. Available online at http://www.Breastcancerindia.net/bc/statistics/stati.html
- 2.National Cancer Institute (NCI). http://www.cancernet.gov
- 5.Gonzalez RC, Woods RE (2002) Digital image processing. Addison-Wesley Publishing Company, BostonGoogle Scholar
- 7.Kaur G, Kumar R, Kainth K (2016) Review paper on different noise types and digital image processing. Int J Adv Res Comput Sci Softw Eng 06(06):562–565Google Scholar
- 9.Charate AP, Jamge SB (2017) The preprocessing methods of mammogram images for breast cancer detection. Int J Recent Innov Trends Comput Commun 5(1):261–264Google Scholar
- 10.Coupe P, Hellier P, Kervrann C, Barillot C (2008) Bayesian non local means-based speckle filtering. In: 5th IEEE international symposium on biomedical imaging. vol 2(8), pp 1291–1294Google Scholar
- 11.Radha M, Adaekalavan S (2016) Mammogram of breast cancer detection based using image enhancement algorithm. Int J Adv Res Comput Commun Eng 5(7):461–466Google Scholar
- 14.Hedaoo P, Godbole SS (2011) Wavelet thresholding approach for image denoising. Int J Netw Secur Appl (IJNSA) 3(4):16–21Google Scholar
- 15.Umapathi VJ, Sathya Narayanan V (2014) Medical image denoising based on gaussian filter and Dwt Swt based enhancement technique. Int J Soft Comput Artif Intell 2(2):1–5Google Scholar
- 16.Kumar HP, Srinivasan S (2012) Performance analysis of filters for speckle reduction in medical polycystic ovary ultrasound images. In: Third international conference on computing, communication and networking technologies (ICCCNT’12) Coimbatore, India. pp 1–5Google Scholar
- 19.Buades A, Coll B, Morel JM (2005) A non-local algorithm for image denoising. In: IEEE international conference on computer vision and pattern recognition, San Diego, CA, USA. vol 38, pp 1724–1731Google Scholar
- 21.Yeganeh H, Ziaei A, Rezaie A (2008) A novel approach for contrast enhancement based on histogram equalization. In: Proceedings of the international conference on computer and communication engineering, Kuala Lumpur, Malaysia. pp 256–260Google Scholar
- 23.University of South Florida Digital Database for Screening mammography. http://marathon.csee.usf.edu/