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Reseach Article

Non-Linear Directive Contrast Filter for Mammogram Images to Enhance Pleomorphic Calcification

by Ramya A., V. Murugan, D. Murugan
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 163 - Number 7
Year of Publication: 2017
Authors: Ramya A., V. Murugan, D. Murugan
10.5120/ijca2017913630

Ramya A., V. Murugan, D. Murugan . Non-Linear Directive Contrast Filter for Mammogram Images to Enhance Pleomorphic Calcification. International Journal of Computer Applications. 163, 7 ( Apr 2017), 52-57. DOI=10.5120/ijca2017913630

@article{ 10.5120/ijca2017913630,
author = { Ramya A., V. Murugan, D. Murugan },
title = { Non-Linear Directive Contrast Filter for Mammogram Images to Enhance Pleomorphic Calcification },
journal = { International Journal of Computer Applications },
issue_date = { Apr 2017 },
volume = { 163 },
number = { 7 },
month = { Apr },
year = { 2017 },
issn = { 0975-8887 },
pages = { 52-57 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume163/number7/27410-2017913630/ },
doi = { 10.5120/ijca2017913630 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:09:34.804467+05:30
%A Ramya A.
%A V. Murugan
%A D. Murugan
%T Non-Linear Directive Contrast Filter for Mammogram Images to Enhance Pleomorphic Calcification
%J International Journal of Computer Applications
%@ 0975-8887
%V 163
%N 7
%P 52-57
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Breast cancer is now wide spread among the women at the age of 35 and above. Initial stage of breast cancer is calcification. Mammography is the precise type of imaging source for breast cancer and calcification deposit in breast, which are usually low contrast mammogram images. This paper deals with the analysis of suspicious or intermediate coarse type of calcification in the breast. Pleomorphic is a kind of the suspicious calcification deposition in the breast, which may lead to cancerous stage, if not examined earlier. Enhancement is one of the pre-processing stage used to improvise the contrast, interpretation and perception of the image, so that calcium deposited areas in the mammogram images can be viewed evidently. Histogram equalization is most significant method for improving the visual perception of medical images. In this paper, before now proposed system of Histogram equalization technique such as RMSHE (Recursive Mean-Separate Histogram equalization), AMHE (Adaptively Modified Histogram Equalization), BPDFHE (Brightness Preserving Dynamic Histogram Equalization) were studied under experimental analysis and compared with our proposed technique such as Non-Linear Directive (NLDC) filter to progress the low-level intensity of an image. Comparison of this technique with the proposed filter is necessary for deciding appropriate algorithms for enhancing the medical images. Quality evaluation factors for image enhancement like PSNR (Peak Signal to Noise Ratio), MSE (Mean Squared Error), Michelson Contrast and AMBE (Absolute Mean Brightness Error) were also analyzed for the existing and proposed technique. The proposed technique yields a better outcome than the other compared technique..

References
  1. 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.
  2. Sreeja, G. Bharatha, P. Rathika, and D. Devaraj. "Detection of tumours in digital mammograms using wavelet based adaptive windowing method." International Journal of Modern Education and Computer Science 4.3 (2012): 57.
  3. Mina, Luqman Mahmood, and Nor Ashidi Mat Isa. "Preprocessing Technique for Mammographic Images."
  4. Wun, Lap-Ming, Ray M. Merrill, and Eric J. Feuer. "Estimating lifetime and age-conditional probabilities of developing cancer." Lifetime data analysis 4.2 (1998): 169-186.
  5. Gaikwad, Ms Nayan H., Ms Anjali P. Narwadkar, and Ms Ashwini S. Barge. "Enhancement of mammogram for detection of breast cancer using adaptive median filter." International Research Journal of Multidisciplinary Studies 2.3 (2016).
  6. Gonzalez, R. C., Woods R. E.: Digital Image Processing. 2nd edn. Prentice-Hall, NewJersey (2002).
  7. Chen, Soong-Der, and Abd Rahman Ramli. "Contrast enhancement using recursive mean-separate histogram equalization for scalable brightness preservation." IEEE Transactions on consumer Electronics 49.4 (2003): 1301-1309.
  8. Kim, Hyoung-Joon, et al. "Contrast enhancement using adaptively modified histogram equalization." Pacific-Rim Symposium on Image and Video Technology. Springer Berlin Heidelberg, 2006.
  9. Ibrahim, Haidi, and Nicholas Sia Pik Kong. "Brightness preserving dynamic histogram equalization for image contrast enhancement." IEEE Transactions on Consumer Electronics 53.4 (2007).
  10. Breast Cancer Facts & Figures, 2013-2014, American Cancer Society, Inc.
  11. Ittannavar, S. S., and R. H. Havaldar. "Comparative Study of Mammogram Enhancement Techniques for Early Detection of Breast Cancer."
  12. 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.
  13. Bandyopadhyay, Samir Kumar. "pre-processing of Mammogram Images." international journal of engineering science and technology 2.11 (2010): 6753-6758.
  14. Akila, K., L. S. Jayashree, and A. Vasuki. "Mammographic image enhancement using indirect contrast enhancement techniques–a comparative study." Procedia Computer Science 47 (2015): 255-261.
Index Terms

Computer Science
Information Sciences

Keywords

Non-Linear Image Enhancement Pleomorphic Calcification Mammogram Histogram Equalization Contrast Intensity.