CFP last date
20 June 2024
Reseach Article

Histogram- A Contrast Enhancement Methods for Mammographic Breast Images

Published on May 2015 by Bhagwati Charan Patel, G.r. Sinha
National Conference Potential Research Avenues and Future Opportunities in Electrical and Instrumentation Engineering
Foundation of Computer Science USA
ACEWRM2015 - Number 3
May 2015
Authors: Bhagwati Charan Patel, G.r. Sinha
16582866-e6d6-4b7b-92ae-f9a4fbae0197

Bhagwati Charan Patel, G.r. Sinha . Histogram- A Contrast Enhancement Methods for Mammographic Breast Images. National Conference Potential Research Avenues and Future Opportunities in Electrical and Instrumentation Engineering. ACEWRM2015, 3 (May 2015), 25-28.

@article{
author = { Bhagwati Charan Patel, G.r. Sinha },
title = { Histogram- A Contrast Enhancement Methods for Mammographic Breast Images },
journal = { National Conference Potential Research Avenues and Future Opportunities in Electrical and Instrumentation Engineering },
issue_date = { May 2015 },
volume = { ACEWRM2015 },
number = { 3 },
month = { May },
year = { 2015 },
issn = 0975-8887,
pages = { 25-28 },
numpages = 4,
url = { /proceedings/acewrm2015/number3/20914-6046/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference Potential Research Avenues and Future Opportunities in Electrical and Instrumentation Engineering
%A Bhagwati Charan Patel
%A G.r. Sinha
%T Histogram- A Contrast Enhancement Methods for Mammographic Breast Images
%J National Conference Potential Research Avenues and Future Opportunities in Electrical and Instrumentation Engineering
%@ 0975-8887
%V ACEWRM2015
%N 3
%P 25-28
%D 2015
%I International Journal of Computer Applications
Abstract

Enhancement of images is applied over all the mammographic images before their diagnosis. The contrast of mammograms is always required to be good so that further investigation of breast cancer images is accurate. Here HE (histogram equalization), HS (histogram specification) and LE (local enhancement) methods are discussed for enhancing and improving the quality of mammographic images and their result and performance are compared with statistical parameter SNR (signal to noise ratio) and RMSE (root mean square error). In histogram techniques, the flexibility of this image processing approach is emphasized to enhance the images. The experimental result indicates that the algorithm can not only enhance image information effectively but also keep the original image luminance well enough fine structure of the image.

References
  1. J. Scharcanski, C. R. Jung (2006) Denoising and enhancing digital mammographic images for visual screening, Computerized Medical Image and Graphics, 30(4): 243–254.
  2. B. C. Patel, G. R. Sinha (2014) Medical Image Processing: concepts and Applications, PHI Learning Private Limited, 1st Edition.
  3. Dengler J, Behrens S, Desage JF. (2004), Segmentation of micro-calcifications in mammograms, IEEE Transaction on Medical Imaging,12(2),pp. 634-42.
  4. J. H. Yoon ,Y. M. Ro (2002) Enhancement of the Contrast in Mammographic Images Using the Homomorphic Filter Method, IEICE Transaction On Information &System, E85(D):356-367.
  5. M. R. Hoque, M. R. Mahfuz (2011) A New Approach in Spatial Filtering to Reduce Speckle Noise, International Journal of Soft Computing and Engineering (IJSCE), 1:29-32.
  6. W. Dabour (2008) Improved Wavelet Based Thresholding for Contrast Enhancement of Digital Mammograms, In Proceedings of International Conference on Computer Science and Software Engineering, 2:187-196.
  7. H. Kyung, N. N. Thanh, S. M. Kim , Y. M. Ro (2004) Robust Contrast Enhancement for Micro calcification in Mammography, In Proceedings of International conference on Computational Science and Its Applications (ICCSA). pp 238-245. .
  8. Cheng, H. D. , Shi, X. J. , Min, R. , Hu, L. M. , Cai, X. P. , Du, H. N. (2006), Approaches for Automated Detection and Classification of Masses in Mammograms. Journal of Pattern Recognition, 39(4), pp. 646–668.
  9. Rangayyan, R. M. , Ayres, F. J. , Desautels, J. E. L. (2007), A Review of Computer-Aided Diagnosis of Breast Cancer: Toward the Detection of Subtle Signs. Journal of the Franklin Institute , 344,pp. 312–348.
  10. Bruce, L. M. , & Adhami, R. R. (1999), Classifying Mammographic Mass Shapes Using the Wavelet Transform Modulus-Maxima Method. IEEE Transaction on Medical Imaging ,18(12), pp. 1170-1177.
  11. B. C. Patel, G. R. Sinha (2014) Mammography Feature Analysis and Mass Detection in Breast Cancer Images. In Proceedings of International Conference on Electronic Systems, Signal Processing and Computing Technologies (ICESC 2014), IEEE conference, Nagpur, 9-11 Jan-14, pp 474-478.
  12. S. Liu, C. F. Babbs, and E. J. Delp, (2001), Multiresolution detection of speculated lesions in digital mammograms, IEEE Trans. Image Process. , 10(6), pp. 874–884.
  13. K. Bovis and S. Singh, "Detection of masses in mammograms using texture features," in Proc. 15th International Conference on Pattern Recogition , (2000),2, pp. 267–270.
  14. M. Zhang, M. L. Giger, C. J. Vyborny, and K. Doi, "Mammographic texture analysis for the detection of spiculated lesions," in Proc. 3rd Int. Workshop Digital Mammography, Chicago, IL, Jun. 9–12, (1996), pp. 347–350.
  15. F. J. Ayres and R. M. Rangayyan,(2005), Characterization of architectural distortion in mammograms, IEEE Eng. Med. Biol. Mag. , 24(1), pp. 59–67.
  16. Bhagwati Charan Patel, Dr. G. R. Sinha. (2011), Comparative Performance Evaluation of segmentation methods in Breast Cancer Images. International Journal of Machine Intelligence. Bioinfo Publication 3(3), PP. 130-133.
  17. D. Guliato, R. M. Rangayyan, J. D. Carvalho, and S. A. Santiago,(2008), Polygonal modeling of contours of breast tumors with the preservation of spicules, IEEE Transaction on Biomedical Engineering. ,55(1), pp. 14–20.
  18. H. Kobatake, M. Murakami, H. Takeo, and S. Nawano, (1999), Computerized detection of malignant tumors on digital mammograms, IEEE Transaction on Medical Imaging,18( 5), pp. 369–378.
  19. H. Li, Y. Wang, K. J. Ray Liu, S. -C. B. Lo, and M. T. Freedman,(2008) , Computerized radiographic mass detection—Part I: Lesion site selection by morphological enhancement and contextual segmentation, IEEE Transaction on Medical Imaging, 20(4), pp. 289–301.
Index Terms

Computer Science
Information Sciences

Keywords

Contrast Enhancement Mammographic Images Histogram Snr (signal To Noise Ratio)