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Towards the Detection of Architecture Distortion in Mammograms: A Review

by Amit Kamra, Sukhwinder Singh, V K Jain
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 46 - Number 7
Year of Publication: 2012
Authors: Amit Kamra, Sukhwinder Singh, V K Jain
10.5120/6924-9356

Amit Kamra, Sukhwinder Singh, V K Jain . Towards the Detection of Architecture Distortion in Mammograms: A Review. International Journal of Computer Applications. 46, 7 ( May 2012), 44-49. DOI=10.5120/6924-9356

@article{ 10.5120/6924-9356,
author = { Amit Kamra, Sukhwinder Singh, V K Jain },
title = { Towards the Detection of Architecture Distortion in Mammograms: A Review },
journal = { International Journal of Computer Applications },
issue_date = { May 2012 },
volume = { 46 },
number = { 7 },
month = { May },
year = { 2012 },
issn = { 0975-8887 },
pages = { 44-49 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume46/number7/6924-9356/ },
doi = { 10.5120/6924-9356 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:39:11.259624+05:30
%A Amit Kamra
%A Sukhwinder Singh
%A V K Jain
%T Towards the Detection of Architecture Distortion in Mammograms: A Review
%J International Journal of Computer Applications
%@ 0975-8887
%V 46
%N 7
%P 44-49
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Breast cancer is most frequently occurring disease among women. Early detection is a reliable method which can prevent breast cancer spreading to distant organs. Mammography is considered as the most reliable method for early detection. The purpose of this paper is to succinctly review recent progress and current state of art knowledge related to detection of architectural distortion in mammograms. Though it is a subtle finding owing to its random nature, it is actually the third most common way that breast cancer appears

References
  1. National Cancer Registry Pogramme. www. ncrpindia. org
  2. Learn about mammograms-National cancer Institute-www. cancer. gov/cancertopics/screening/breast/mammogram 2008.
  3. Vidya R. Pai, Nancy E. Gregory, Ann E. Swinford, Murray Rebner, Ductal Carcinoma in Situ: Computer–aided Detection in Screening Mammography, Radiology, Vol: 241(3) 2006. 689-694.
  4. Mehul P. Sampat, Mia K. Markey and Alan C. Bovik, 2005. "Computer-Aided Detection and Diagnosis in Mammography", Elsevier Academic Press, pp. 1195-1217.
  5. American College of Radiology (ACR), Illustrated Breast Imaging Reporting and Data System (BI-RADS), 1998.
  6. http://www. breast-cancer. ca/screening-index. htm,2011.
  7. www. radiographics. rsna. org.
  8. Birdwell RL, Ikeda DM, O'Shaughnessy KF, Sickles EA. Mammographic characteristics of 115 missed cancers later detected with screening mammography and the potential utility of computer-aided detection Radiology. 219 (1):192-202, 2001
  9. Yasuyo Kita, Eriko Tohno, Ralph P. Highnam, Michael Brady. A CAD system for the 3D location o lesions in mammograms. Medical Image analysis, Vol 6. issue 3,pp267-273 2002
  10. Evans WP, Warren Burhenne LJ, Laurie L, O'Shaughnessy KF, Castellino RA. Invasive lobular carcinoma of the breast: mammographic characteristics and computer-aided detection. Radiology. Vol, 225(1):182-9. 2002
  11. Ciatto S, Del Turco MR, Risso G, Catarzi S, Bonardi R, Viterbo V, Gnutti P, Guglielmoni B, Pinelli L, Pandiscia A, Navarra F, Lauria A, Palmiero R, Indovina PL. Comparison of standard reading and computer aided detection (CAD) on a national proficiency test of screening mammography Eur J Radiol. Feb; 45(2):135-8, 2003.
  12. A. Baker,E. L. Rosen,J. Y. Lo,E. I. Gimenez,R,Walsh, CAD in screening mammography: Sensitivity of commercial CAD systems for detecting architectural distortion, Am. J. Roentgenol. 181,1083-1088,2003
  13. Hans Bornefalk Estimation and comparison of CAD system performance in clinical settings Academic Radiology,Vol 12,Issue 6 PP 687-694,2005.
  14. R2 Technology website, http://www. r2tech. com/ 2006.
  15. R. M. Rangaayan, F. J. Ayres, J. E. L Desautels. A review of computer aided diagnosis of breast cancer: Towards the detection of subtle signs. Journal of Frankin institute, 344, 3112-348, 2006.
  16. Kunio Doi, Computer-aided diagnosis in medical imaging: Historical review, current status and future potential Computerized Medical Imaging and Graphics 31, 198–211, 2007.
  17. G. Ram, Optimization of ionizing radiation usage in medical imaging by means of image enhancement techniques,Med. Physics,Vol 9(5), 733-737,1982.
  18. Sheng-Chih-Yang, Chuin-Mu Wang, Yi-Nung Chung, Giu-Cheng Hsu, San-Kan Lee, Pau- Choo Chung and Chein-1 Chang, 2005. "A Computer Aided System for Mass Detection and classification in Digitized Mammograms", Biomedical Engineering- Applications, Basis and Communications 17, pp. 215-228.
  19. Alfonso Rojas Dominguez and Asoke K. Nandi, 2008. "Detection of masses in mammogram via statistically based enhancement, multilevel-thresholding segmentation, and region selection", Computerized medical imaging and graphics 32, pp. 304-315.
  20. S. M. Pizer, E. O. P. Amburn and J. D. Austin, 1987. "Adaptive histogram equalization and its variations", Comput. Vision Graphics Image Process. 39, pp. 355-368.
  21. Paul Sajda, Clay Spence and Lucas Parra, 2003. "A Multi-Scale Probabilistic Network Model for Detection, Synthesis and Compression in Mammographic Image Analysis", Medical Image Analysis 7, pp. 187-204.
  22. Nico Karssemeijer ,Guido M. te Brake Detection of stellate distortion in mammograms ,IEEE Transactions on medical imaging Vol. 15,No 5 pp 611-619 1996.
  23. T. Matsubara, T. Ichikawa, T. Hara, H. Fujita, S. Kasai,T. Endo, T. Iwase, Automated detection methods for architectural distortions around skinline and within mammary gland on mammograms. International Congress Series Vol. 1256, pp. 950–955, 2003.
  24. F. Kelcz, P. A. Propeck, E. S. Burnside, The Role of MRI in the Workup of Mammographic Architectural Distortion. Proc. Intl. Soc. Mag. Reson. Med. 11 pp 280, 2003.
  25. Sampat M. P. Bovik, A. C. Detection of spiculated lesions in mammograms, Engineering in Medicine and Biology Society, Proceedings of the 25th Annual International Conference of the IEEE. 2003.
  26. Tetsuko Ichikawa, Tomoko Matsubara, Takeshi Hara, Hiroshi Fujita, Tokiko Endo, Takuji Iwase, Automated detection method for architectural distortion areas on mammograms based on morphological processing and surface analysis, Medical Imaging: Image Processing, edited by J. Michael Fitzpatrick, Milan Sonka, Proceedings of SPIE Vol. 5370 2004.
  27. Jane Wang, Laurie L. , Fajardo, Laila Dahmoush, Michael W. Vannier, Lobular carcinoma in situ presenting as architectural distortion on mammograms: a case report and review of the literature. European Journal of Radiology,Vol. 50,Issue 2,pp 63-66. 2004.
  28. Sheng. -Fang, Huang. Ruey, . Feng. ,Chang,. Dar. Ren. , Chen,. Woo. , Kyung,. Moon. , Characterization of speculated lesions on ultrasound lesions. IEEE transactions on medical imaging, Vol. 23, Issue 1. 2004.
  29. Lihua Li, Zuobao Wu,Li Chen,Florence George,Zhao Chen,Angela Salem,Maria Kallergi,Claudia Berman Breast Tissue density and CAD cancer detection in digital mammography , Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference Shanghai,China,pp 3253-3256, 2005
  30. F. J. Ayres and R. M. Rangayyan, "Characterization of architectural distortion in mammograms," IEEE Eng. Med. Biol. Mag. , vol. 24, no. 1, pp. 59–67, 2005.
  31. T. Matsubara, D. Fukuoka, N. Yagi,T. Hara, H. Fujita, S. Kasai,T. Endo, T. Iwase Detection method for architectural distortion based on analysis of structure of mammary gland on mammograms. International Congress Series Vol. 1281 ,pp 1036-1040, 2005
  32. M. P. Sampat, G. J. Whitman,M. K. Markey, and A. C. Bovik, "Evidence based detection of spiculated masses and architectural distortion," SPIE Med. Imag. 2005: Image Process. , vol. 5747, pp 26-37 2005.
  33. Q. Guo, J. Shao, V. Ruiz. Investigation of support vector machine for the detection of architectural distortion in mammographic images. Journal of physics: Conference Series,Vol. 15 pp 88-94, 2005
  34. Sampat, M. P. Bovik, A. C. Markey, M. K. Whitman, G. J. Stephens, Toroidal Gaussian Filters for Detection and Extraction of Properties of Spiculated Masses, IEEE International Conference on Acoustics, Speech and Signal Processing, Toulouse, France,2006
  35. R. M. Rangayyan and F. J. Ayres, "Gabor filters and phase portraits for the detection of architectural distortion in mammograms," Med. Biol. Eng. Comput. , vol. 44, no. 10, pp. 883–894, 2006.
  36. F. J. Ayres and R. M. Rangayyan, "Reduction of false positives in the detection of architectural distortion in mammograms by using a geometrically constrained phase portrait model," Int. J. Comput. Assist. Radiol. Surg. , vol. 1, no. 6, pp. 361–369, 2007.
  37. Georgia D Tourassi1, David M Delong2 and Carey E Floyd Jr A study on the computerized fractal analysis of architectural distortion in screening mammograms, Vol. 51,Issue 5,pp 1299-1312, 2006.
  38. N. Eltonsy,G Tourasi, A . Elmaghraby. Invetsigating performance of a morphological based CAD scheme in detecting architectural distortion in screening mammograms. Proceddings of the 20th International Congress and Exhibition on Computer assisted radiology and Surgery, pp 336-338. 2006.
  39. Essam A. Rashed,Ismail A. Ismail Sherif I Zaki Multiresolution mammogram analysis in multilevel decomposition Elsevier Pattern Recognition Vol, 28 pp. 286-292, 2007
  40. Banik,S. Rangayyan, R. M. Desautels, J. E. L, Detection of architectural distortion in prior mammograms of interval-cancer cases with neural networks, Engineering in Medicine and Biology Society, EMBC, Annual International Conference of the IEEE, 2009
  41. Banik, S; Rangayyan, R. M. ; Desautels, J. E. L. ; Detection of Architectural Distortion in Prior Mammograms IEEE Medical Imaging,Vol. 30,pp 279-294,2011.
  42. Biswas. ,S. Mukherjee, D. Recognizing Architectural Distortion in Mammogram: A Multi-Scale Texture Modeling Approach with GMM. IEEE Transactions of Biomedical Engineering,Vol. 99 Issue. 1 2011.
Index Terms

Computer Science
Information Sciences

Keywords

Breast Cancer Cad (computer Aided Diagnosis) Architectural Distortion Mammography

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