Review on Computer Aided Detection Systems of Breast Cancer

Authors

  • A.M.Solanke Jain - a Deemed to be University Bangalore, India. Author
  • Dr.R.Manjunath Jain - a Deemed to be University Bangalore, India. Author
  • Dr.D.V.Jadhav Jain - a Deemed to be University Bangalore, India. Author

DOI:

https://doi.org/10.61841/7s9zzk75

Keywords:

Architectural distortion detection, CAD(Computer aided detection), Mammography,Mass, Microcalcifications

Abstract

Breast cancer is life threatening disease for women.According to World Health Organisation breast cancer is second leading cause of death in the world.Many lives can be saved by early detection of breast cancer.Most widely used breast cancer screening technique is mammography. Mammography is used for detection and clinical evaluation of breast cancer. Computer aided detection techniques(CAD) are used to assist doctors and radiologists for analysing mammograms.CAD techniques plays very important role in early detection of breast cancer.In this paper total forty five papers are referred to present overview of signs of breast cancer,screening technique and survey of algorithms for detection of Micro-calcifications,masses and architectural distortion.

     

 

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References

1. Turchetti G., Spadoni E and Geisler E.(2010) ‘Health technology assessment: evaluation of biomedical innovative technologies’ IEEE Eng. Med. Biol. Mag.,vol. 29, no. 3, PP. 70-76.

2. Bertoldo Schneider , Fábio Kurt Schneider , Carlos Eduardo de Andrade Lima da Rocha, Carlos Alberto Dallabona(2010) ‘The Role of Biomedical Engineering in Health System Improvement and Nation’s Development’ 32nd Annual International Conference of the IEEE EMBS Buenos Aires, Argentina, August 31 - September 4, PP.6248-6251.

3. www.who.int

4. Michiel Kallenberg, Kersten Petersen, Mads Nielsen, Andrew Y. Ng, Pengfei Diao, Christian Igel, Celine

M. Vachon, Katharina Holland, Rikke Rass Winkel, Nico Karssemeijer, and Martin Lillholm(2016)‘Unsupervised Deep Learning Applied to Breast Density Segmentation and Mammographic Risk Scoring’ IEEE Transactions on medical imaging, vol. 35, PP. 1322-1331.

5. Rinku Rabidas, Abhishek Midya, and Jayasree Chakraborty(2017) ‘Neighborhood Structural Similarity Mapping for the Classification of Masses in Mammograms’IEEE journal of biomedical and health informatics,Volume: 22, Issue: 3.PP.1-12.

6. Jasjit S.Suri,R.M.Rangayyan ‘Recent Advances in Breast Imaging, Mammography, and Computer-Aided Diagnosis of Breast Cancer’,Bellingham,Washington,SPIE Press,2006.

7. Mencattini A, Salmeri M, Lojacono R, Frigerio M, Caselli F.(2008) ‘Mammographic images enhancement and de noising for breast cancer detection using dyadic wavelet processing’IEEE Transaction on Instrumentation Meas.vol.57.No.7,PP.1422-1429.

8. Dhawan, A.P. “ Medical Image Analysis” John Wiley &Sons,Inc., publications Hoboken, New Jersey,2011

9. www.acr.org/Clinical-Resources/Reporting-and-Data-Systems/Bi-Rads

10. Meindert Niemeijer, Marco Loog, Michael David Abràmoff, Max A. Viergever, Mathias Prokop, and Bram van Ginneken(2011) ‘On Combining Computer-Aided Detection Systems’ IEEE Transactions on medical imaging,February, vol. 30, no. 2.PP.215-223.

11. Donato Cascio, Francesco Fauci1, Marius Iacomi1,Giuseppe Raso1,Rosario Magro, Debora Castrogiovanni, Guido Filosto, Raffaele Ienzi & MariaSimoneVasile(2014) ‘Computer-aided diagnosis in digital mammography: comparison of two commercial systems’Imaging Med. Volume 6, Issue 1. PP.13–20.

12. B.Senthilkumar,G.Umamaheswari(2011)‘A Review on Computer Aided Detection and Diagnosis - Towards the Treatment of Breast Cancer’ European Journal of Scientific Research ISSN 1450-216X Vol.52 No.4, PP.437-452

13. Maximilian F Reiser, Gerhard van Kaick, Christian Fink, S.O. Schoenberg(2008)‘Screening and Preventive Diagnosis with Radiological Imaging’Springer Science & Business Media, 03-Jan,PP 106

14. Bozek J., Mustra M., Delac K., Grgic M. (2009) A Survey of Image Processing Algorithms in Digital Mammography. In: Grgic M., Delac K., Ghanbari M. (eds) Recent Advances in Multimedia Signal Processing and Communications. Studies in Computational Intelligence, vol 231. Springer, Berlin, Heidelberg,PP.631-652.

15. Robin N.Stricklan, Hee Hahn (1996) ‘Wavelet Transforms for Detecting Microcalcifications in Mammograms’ IEEE Transactions on medical imaging, vol. 15, no. 2, AprilPP.218-229.

16. Karen Panetta, Yicong Zhou, Member, Sos Agaian, Hongwei Jia(2011) ‘Nonlinear Unsharp Masking for Mammogram Enhancement’IEEE transactions on information technology in biomedicine, vol.15, no.6, Nov.PP.918-927.

17. MencattiniA.,M.Salmeri,R.Lojacono,F.Caselli(2006) ‘Mammographic Images Enhancement and Denoising for Microcalcification Detection Using Dyadic Wavelet Processing’ IMTC 2006-Instrumentation and Measurement Technology Conference Sorrento, Italy PP.24-27.

18. Tiago A.Docusse, Aledir S. Pereira, Norian Marranghello(2009) ‘Microcalcification Border Characterization’IEEE engg. in medicine and biology,vol.28,issue 5,PP.41-43.

19. Rangayyan RM, Ayres FJ, Desautels JEL(2007) ‘A review of computer-aided diagnosis of breast cancer: Toward the detection of subtle signs’. J Franklin Inst 344,PP.312-348.

20. Liyang Wei, Yongyi Yang, Robert M. Nishikawa, Miles N. Wernick, Alexandra Edwards(2005)‘Relevance Vector Machine for Automatic Detection of Clustered Microcalcifications’ IEEE transactions on medical imaging,vol.24,no.10, October,PP.1278-1285.

21. Anna N. Karahaliou, Ioannis S. Boniatis, Spyros G. Skiadopoulos, Filippos N. Sakellaropoulos, Nikolaos S. Arikidis, Eleni A. Likaki, George S. Panayiotakis, and Lena I. Costaridou(2008) ‘Breast Cancer Diagnosis: Analyzing Texture of Tissue Surrounding Microcalcifications’ IEEE transactions on information technology in biomedicine, Vol. 12, no. 6, November, PP.731-738.

22. K.J.McLoughlin,P.J.Bones,N.Karssemeijer(2004) “Noise equalization for detection of microcalcification clusters in direct digital mammogram images,” IEEE Trans. Med. Imag., vol. 23, no. 3,Mar, pp. 313–320.

23. Renbin Peng, Hao Chen, Pramod K. Varshney(2009) ‘Noise-Enhanced Detection of Micro-Calcifications in Digital Mammograms’ IEEE journal of selected topics in signal processing,February,vol.3,no.1, pp.62-73.

24. Ming Li and Zhi-Hua Zhou(2007) ‘Improve Computer-Aided Diagnosis With Machine Learning Techniques Using Undiagnosed Samples’ IEEE Transactions on systems,man,and cybernetics-part A: systems and humans, vol.37,no.6.PP.1088-1098.

25. Nan-Chyuan Tsai, Hong-Wei Chen, Sheng-Liang Hsu(2011)‘Computer-aided diagnosis for early-stage breast cancer by using Wavelet Transform’Computerized Medical Imaging and Graphics,vol. 35 PP. 1–8.

26. Liyang Wei, Yongyi Yang, Robert M. Nishikawa, and Yulei Jiang(2005)‘A Study on Several Machine-Learning Methods for Classification of Malignant and Benign Clustered Microcalcifications’Transactions on medical imaging,March,vol. 24, no. 3, pp.371-380.

27. Zhili Chen, Harry Strange, Arnau Oliver, Erika R. E. Denton, Caroline Boggis, and Reyer Zwiggelaar(2015) ‘Topological Modeling and Classification of Mammographic Microcalcification Clusters’ IEEE Transactions on biomedical engineering, vol. 62, no. 4,PP.1023-1214.

28. Nicholas Petrick,Heang-Ping Chan,Berkman Sahiner,and Datong Wei(1996)‘An Adaptive Density-Weighted Contrast Enhancement Filter for Mammographic Breast Mass Detection’ IEEE Transactions on medical imaging,February , vol. 15, no.1,pp59-67.

29. I.Christoyianni,E.Dermatus,G.Kokkinakis (2000)‘Fast detection of masses in computer aided mammography’ IEEE signal processing magazine,January,PP

30. Naga R. Mudigonda, Rangaraj M. Rangayyan, and J. E. Leo Desautels(2000)“Gradient and Texture Analysis for the Classification of Mammographic Masses”IEEE Transactions on medical imaging, october

,vol.19,no.10, pp.1032-1043.

31. Peter Mc Leod and Brijesh Verma,(2013), ‘Variable Hidden Neuron Ensemble for Mass Classification in Digital Mammograms’ IEEE ComputatIonal Intelligence magazine,February,Vol. 8 Issue 1, pp. 68-76

32. Sujoy Kumar Biswas, Dipti Prasad Mukherjee(2011)‘Recognizing Architectural Distortion in Mammogram: A Multiscale Texture Modeling Approach with GMM’ IEEE Transactions on biomedical engineering, vol. 58, no. 7, July pp.20-23.

33. Magdalena Jasionowska, Artur Przelaskowski,Aleksandra Rutczynska, and Anna Wroblewska (2010)‘A Two-Step Method for Detection of Architectural Distortions in Mammograms’ E. Pi˛etka and J. Kawa (Eds.): Information Technologies in Biomedicine, AISC 69, pp. 73–84.

34. Fabio J.Ayrres. Rangaraj M. Rangayyan (2007) ‘Reduction of false positives in the detection of architectural distortion in mammograms by using a geometrically constrained phase portrait model’, International Journal of Computer Assisted Radiology and Surgery, Vol. 1, pp. 361–369.

35. Mitsutaka Nemoto,Soshi Honmura, Akinobu Shimizu,Daisuke Furukawa, Hidefumi Kobatake,Shigeru Nawano(2008) ‘A pilot study of architectural distortion detection in mammograms based on characteristics of line shadows’ Int. J.for Computer Assisted Radiology and Surgery (IJCARS) January,Volume 4, Issue 1, pp 27–36.

36. Orawan Netprasat, Sansanee Auephanwiriyakul, Nipon Theera-Umpon(2014) ‘Architectural Distortion Detection from Mammograms Using Support Vector Machine’ International Joint Conference on Neural Networks (IJCNN) July 6-11, 2014, Beijing, China.

37. Xiaoming Liu, Leilei Zhai, Ting Zhu,Zhou Yang (2016)‘Architectural Distortion Recognition based on a Subclass Technique and the Sparse RepresentationClassifier’9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics(CISP-BMEI 2016).

38. Rami Ben-Ari, Ayelet Akselrod-Ballin, Leonid Karlinsky, Sharbell Hashoul(2017)‘Domain specific convolutional neural nets for detection of architectural distortion in mammograms’IBM Research - Haifa, Israel IEEE conference, pp.552-556.

39. R.M.Rangayyan, S. Prajna, F. J. Ayres, and J. E. L. Desautels(2008)‘Detection of architectural distortion in mammograms acquired prior to the detection of breast cancer using Gabor filters, phase portraits, fractal dimension, and texture analysis’ Int. J. Comput. Assist. Radiol. Surg.,vol. 2, no. 6, pp. 347–361.

40. Rangraj. M. Rangayyan, S. Banik, and J. E. L. Desautels(2010) ‘Computer-aided detection of architectural distortion in prior mammograms of intervalcancer’ Journal of Digital Imaging, Vol 23, No 5 (October), 2010: pp 631- 611.

41. Shantanu Banik, Rangaraj M. Rangayyan, and J. E. Leo Desautels(2011) ‘Detection of Architectural Distortion in Prior Mammograms’ IEEE Transactions on medical imaging, vol. 30, no. 2,pp.279-284.

42. R.M.Rangayyan,S.Banik,J.Chakraborty,S.Mukhopadhyay,and J.E.Desautels(2012)‘Measures of divergence of oriented patterns for the detection of distortion in prior mammograms’ Int.J. Comput. Assist. Radiol. Surg., vol. 8, no. 4, pp.527–545.

43. https://radiopaedia.org

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Published

31.10.2020

How to Cite

A.M.Solanke, R.Manjunath, & D.V.Jadhav. (2020). Review on Computer Aided Detection Systems of Breast Cancer. International Journal of Psychosocial Rehabilitation, 24(8), 14116-14124. https://doi.org/10.61841/7s9zzk75