We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 November 2024
Call for Paper
December Edition
IJCA solicits high quality original research papers for the upcoming December edition of the journal. The last date of research paper submission is 20 November 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Review: On Performance Metrics for Quantitative Evaluation of Contrast Enhancement in Mammograms

by Ankita Pandey, Sarbjeet Singh, Brijendrapal Singh
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 75 - Number 17
Year of Publication: 2013
Authors: Ankita Pandey, Sarbjeet Singh, Brijendrapal Singh
10.5120/13205-0766

Ankita Pandey, Sarbjeet Singh, Brijendrapal Singh . Review: On Performance Metrics for Quantitative Evaluation of Contrast Enhancement in Mammograms. International Journal of Computer Applications. 75, 17 ( August 2013), 40-45. DOI=10.5120/13205-0766

@article{ 10.5120/13205-0766,
author = { Ankita Pandey, Sarbjeet Singh, Brijendrapal Singh },
title = { Review: On Performance Metrics for Quantitative Evaluation of Contrast Enhancement in Mammograms },
journal = { International Journal of Computer Applications },
issue_date = { August 2013 },
volume = { 75 },
number = { 17 },
month = { August },
year = { 2013 },
issn = { 0975-8887 },
pages = { 40-45 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume75/number17/13205-0766/ },
doi = { 10.5120/13205-0766 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:44:32.578357+05:30
%A Ankita Pandey
%A Sarbjeet Singh
%A Brijendrapal Singh
%T Review: On Performance Metrics for Quantitative Evaluation of Contrast Enhancement in Mammograms
%J International Journal of Computer Applications
%@ 0975-8887
%V 75
%N 17
%P 40-45
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The most common cancer of women is breast cancer which is the leading cause of cancer-related death among women aged 15 to 54. The risk of cancer increases after the age of 40's. Thus earlier detection of breast cancer increases the probability of survival of the patient. For its detection mammography is done, but many of the masses remain either undetected or falsely detected due to poor contrast and noise present in mammographic images. Thus for earlier detection of cancerous masses many enhancement techniques are applied. In this paper various set of performance metrics that measure the quality of the image enhancement of mammographic images in a CAD framework that automatically finds masses using machine learning techniques. These performance metrics quantitatively measures the best suited image enhancement on a per mammogram basis, which improves the quality of ensuing image segmentation much better than using the same enhancement method for all mammograms.

References
  1. Panetta. K. , Yicong Zhou, Agaian. S. , Hongwei Jia, "Nonlinear Unsharp Masking for Mammogram Enhancement", IEEE Transactions on Information Technology in Biomedicine, Volume. 15, pp. 918- 928, 2011.
  2. B. Zheng, Y. H. Chang, M. Staiger, W. Good, and D. Gur, "Computeraided detection of clustered microcalcifications in digitized mammograms,"Acad. Radiol. , vol. 2, no. 8, pp. 655–662, 1995.
  3. W. Zhang, K. Doi, M. Giger, R. Nishikawa, and R. Schmidt, "An improved shift-invariant artificial neural network for computerized detection of clustered microcalcifications in digital mammograms," Med. Phys. , vol. 23, no. 4, pp. 595–601, 1996.
  4. A. Webb, Statistical Pattern Recognition. London, U. K. : Arnold, 1999.
  5. R. H. Nagel, R. M. Nishikawa, J. Papaioannou, and K. Doi, "Analysis of methods for reducing false positives in the automated detection of clustered microcalicifications in mammograms," Med. Phys. , vol. 25, no. 8, pp. 1502–1506, 1998.
  6. A. J. Mendez, P. G. Tahoces, M. J. Lado, M. Souto, and J. J. Vidal, "Computer-aided diagnosis: Automatic detection of malignant masses in digitized mammograms," Med. Phys. , vol. 25, no. 6, pp. 957–964, 1998.
  7. M. J. Bottema, "Detection and classification of lobular and dcis (small cell) microcalcifications in digital mammograms," Pattern Recognit. Lett. , vol. 21, pp. 1209–1214, 2000.
  8. W. Zhang, H. Yoshida, R. M. Nishikawa, and K. Doi, "Optimally weighted wavelet transform based on the supervised training for detection of microcalcifications in mammograms," Med. Phys. , vol. 25, no. 6, pp. 949–955, 1998.
  9. Y. C. Wu,M. T. Freedman, A. Hasegawa, R. A. Zuurbier, L. Shih-Chung, B. Lo, and S. K. Mun, "Classification of microcalcifications in radiographs of pathologic specimens for the diagnosis of breast cancer," Acad. Radiol. , vol. 2, no. 3, pp. 199–204, 1995.
  10. J. Parker, D. R. Dance, D. H. Davies, L. J. Yeoman, M. J. Mitchell, and S. Humphreys, "Classification of ductal carcinoma in situ by image analysis of calcifications from digital mammograms," Br. J. Radiol. , vol. 68, pp. 150–159, 1995.
  11. J. M. Mossi and A. Albiol, "Improving detection of clustered microcalcifications using morphological connected operators," in Proc. Inst. Elect. Eng. '99, pp. 465–501, 1999.
  12. Y. Jiang, R. M. Nishikawa, D. E. Wolverton, C. E. Metz, M. L. Giger, R. A. Schmidt, C. J. Vyborny, and K. Doi, "Malignant and benign clustered microcalcifications: Automated feature analysis and classification," Radiology, vol. 198, no. 3, pp. 671–678, 1996.
  13. D. Betal, N. Roberts, and G. H. Whitehouse, "Segmentation and numerical analysis microcalcifications on mammograms using mathematical morphology," Br. J. Radiol. , vol. 70, pp. 903–917, 1997.
  14. T. Hastie, D. Ikeda, and R. Tibshirani, "Statistical measures for the computer-aided diagnosis of mammographic masses," J. Comput. Graphical Statist. , vol. 8, no. 3, pp. 531–543, Sep. 1999.
  15. N. Petrick, H. P. Chan, D. Wei, B. Sahiner, M. A. Helvie, and D. D. Adler, "Automated detection of breast masses on mammograms using adaptive contrast enhancement and texture classification," Med. Phys. , vol. 23, no. 10, pp. 1685–1696, 1996.
  16. W. E. Polakowski, D. A. Cournoyer, S. K. Rodgers, M. P. Desimio, D. W. Ruck, J. W. Hoffmeister, and R. A. Raines, "Computer-aided breast cancer detection and diagnosis of masses using difference of Gaussians and derivative-based feature saliency," IEEE Trans. Med. Imag. , vol. 16, no. 6, pp. 811–819, Dec. 1997.
  17. G. M. te Brake and N. Karssemeijer, "Single and multiscale detection of masses in digital mammograms," IEEE Trans. Med. Imag. , vol. 18, no. 7, pp. 628–639, Jul. 1999.
  18. D. Wei, H. P. Chan, N. Petrick, B. Sahiner, M. A. Helvie, D. D. Adler, and M. M. Goodsitt, "False positive reduction technique for detection of masses on digital mammograms: global and local multiresolution texture analysis," Med. Phys. , vol. 24, no. 6, pp. 903–914, 1997.
  19. H. P. Chang, D. Wei, M. A. Helvie, B. Sahiner, D. D. Adler, M. Goodsitt, and N. Petrick, "Computer-aided classification of mammographic masses and normal tissue: Linear discriminant analysis in texture feature space," Phys. Med. Biol. , vol. 40, pp. 857–876, 1995.
  20. T. Matsubara, "Development of new schemes for detection and analysis of mammographic masses," in Proc. Int. Conf. Intelligent Information Systems (IIS'97), Bahamas, 1997.
  21. N. Petrick, H. P. Chan, B. Sahiner, and M. A. Helvie, "Combined adaptive enhancement and region growing segmentation of breast masses on digitized mammograms," Med. Phys. , vol. 26, no. 8, pp. 1642–1654, 1999.
  22. B. Sahiner, H. P. Chan, N. Petrick, M. A. Helvie, and M. M. Goodsitt, "Computerized characterization of masses on mammograms: the rubber band straightening transform and texture analysis," Med. Phys. , vol. 25, no. 4, pp. 516–526, 1998.
  23. J. Tou and R. Gonzalez, Pattern Recognition Principles. Reading, MA: Addison-Wesley, 1974.
  24. F. F. Yin, M. L. Giger, C. J. Vyborny, K. Doi, and R. A. Schmidt, "Comparison of bilateral subtraction techniques in the computerized detection of mammographic masses," Investigative Radiol. , vol. 28, no. 6, pp. 473–481, 1993.
  25. Vikrant Bhateja , Swapna Devi, "An Improved Non-Linear transformation Function for Enhancement of Mammographic Breast Masses", IEEE international conference Electronics Computer Technology (ICECT), pp. 341-346, 2011.
  26. Baskaran. V. , Guergachi. A. , Bali. R. K. , Naguib. R. N. G. ,"Predicting Breast Screening Attendance Using Machine Learning Techniques", IEEE Transactions on Information Technology in Biomedicine, Volume. 15 , pp. 251–259, 2011.
  27. American Cancer Society: Cancer Facts and Figures, 2002.
  28. Angelo. M. F. , Patrocinio. A. C. , Schiabel. H. , Medeiros. R. B. , Pires. S. R,"Comparing Mammographic Images", IEEE Magazine Transaction on Image Processing, Engineering in Medicine and Biology, pp. 74–81, 2008.
  29. Gonzalez and Woods, Digital Image Processing, Pearson Education, India, 2009, Chapter 5, pp. 343-459.
  30. Singh,S. ,K. Bovis,"An Evaluation of Contrast Enhancement Techniques for Mammographic Breast Masses", IEEE Transaction on Information Technology in Biomedicine, Volume. 9, pp. 109-119 ,2005.
  31. M. J. Lado, P. G. Tahoces, A. J. Mendez, M. Souto, and J. J. Vidal, "A wavelet-based algorithm for detecting clustered microcalcifications in digital mammograms," Med. Phys. , vol. 26, no. 7, pp. 1294–1305, 1999.
  32. Wang, Zhou ; Bovik, AlanC Conrad ; Sheikh, Hamid Rahim ;Simoncelli, Eero P. , "Image quality assessment: from error visibility to structural similarity", IEEE Transactions on Image Processing, Volume: 13, Page(s): 600 – 612, 2004.
  33. Lai,ShukMei ; Li,Xiaobo ; Biscof,W. F. , "On techniques for detecting circumscribed masses in mammograms", IEEE Transactions on Medical Imaging, Volume: 8 , Page(s): 377 – 386, 1989.
  34. Petrick, Nicholas A. ; Chan, Heang-Ping ; Sahiner, Berkinan ;Wei, Datong, "An adaptive density-weighted contrast enhancement filter for mammographic breast mass detection", IEEE Transactions on Medical Imaging, Volume: 15 , Page(s): 59 – 67, 1996.
Index Terms

Computer Science
Information Sciences

Keywords

MLO CC ASNR PSNR ROI DSM CEM CII CD TBCs TBC

Powered by PhDFocusTM