Analysis of Breast Cancer And Image Processing Techniques
|© 2020 by IJCTT Journal|
|Year of Publication : 2020|
|Authors : Mrs. T. Leena Prema Kumari, Dr. K. Perumal|
|DOI : 10.14445/22312803/IJCTT-V68I1P113|
How to Cite?
Mrs. T. Leena Prema Kumari, Dr. K. Perumal, "Analysis of Breast Cancer And Image Processing Techniques," International Journal of Computer Trends and Technology, vol. 68, no. 1, pp. 58-63, 2020. Crossref, https://doi.org/10.14445/22312803/IJCTT-V68I1P113
Rapid increase in the number or amount of cell in their growth and the structure is a cancer cell. Prior detection of cancer can reduce the demise rate. Radiologist can miss the abnormalities due to inexperience in the field of mammography to detect the cancer. Many people have been cured of it due to early detection. Still the automated classification of Mss is a complex task. Dense tissues may easily be confused as calcification result in high false positive. So, pre- processing to enhance the images places a vital role to adjust and make the correction by avoid the unwanted part of image. The success of segmentation and classification depends upon the accuracy of pre-processing. The aim of this process is to enhance in the quality by removing the unrelated and surplus parts in the background of mammogram. Different types of abnormalities, patterns and the features of BI-RADS and various techniques to evaluate the mammogram using image processing were discussed. This paper concludes with the need of pre processing techniques to get the best accuracy.
Full Field Digital Mammography, Computer Aided Detection, Breast Imaging- Reporting and Data System, Region of Interest,Medio Lateral Oblique, Cranio Cauda
 BI-RADS Tutorial by G.Pfarl, MD & T.H. Helbich,MD< Department f Radioloogy, University of Vienna.
 BI-RADS Lexicon for US and Mammography, Interobserver Variability and Positive Predict value by Lazarus, M.B.Mainerio, B.Scheeps, S.L.Kelliker, and L.S.Livingston Radiology, May 1, 2006;239(2):385-391.
 Breast Imaging Reporting and Data System, Inter- and Intraobserver Variability in Feature Analysis and Final Assessmentby Wendie A. Berg et al Department of Radiology, University of Maryland School of Medicine, 22 S. Greene St.,Baltimore,AJR 2000; 174:1769- 1777.
 Balpreet Kauri, and Prabhpreet kau, “A comparative study on Image Segmentation Techniques”, International Journal of computer science and Engineering, Vol. 3, Issue 12, pp.50- 56, Dec 2015.
 Sujata Saini And Komal Arora, “A study Analysis on the different Image Segmentation techniques” International Journal of Information & Computational Technology, Vol. 4, Issue.14, PP 1445-1452,2015.
 Jigar M. Pandya, Devang Rathod, Jigna J. Jadav,” A Survey of Face Recognition approach”, International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 1, January -February 2013, pp.632- 635.
 Jyoti S. Bedre ,Shubhangi Sapkal, ”Comparative Study of Face Recognition Techniques: A Review”, Emerging Trends in Computer Science and Information Technology – 2012(ETCSIT2012) Proceedings published in International Journal of Computer Applications® (IJCA) 12
 A. S. Tolba, A.H. El-Baz, and A.A. El-Harby, ” Face Recognition: A Literature Review”, International Journal of Signal Processing 2:2 2006.
 A. M. Khan, Ravi. S, “Image Segmentation Methods: A Comparative Study,” International Journal of Soft Computing and Engineering (IJSCE) ISSN:2231-2307, Vol.3, Issue 4, September 2013.
 V. Sivakumar and V.Murugesh, “A Brief Study of Image Segmentation using Thresholding Technique on a Noisy Image,” IEEE, 2014.
 Y. Nakagawa and A. Rosenfeld, “Some experiments on variable thresholding, Pattern Recognition,” Vol. 11, 1979, pp. 191-204.
 K. K. Singh, A. Singh, “A Study Of Image Segmentation Algorithms for Different Types Of Images,” IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 5, September 2010.
 S.S. Al-amri, N.V. Kalyankar and Khamitkar S.D, “Image Segmentation by Using Threshold Techniques,” Journal of Computing, Vol. 2, Issue 5, May 2010.
 Mantas Paulinas, Andrius Ušinskas, “A Survey of Genetic Algorithms Applications for Image Enhancement and Segmentation” ISSN 1392-124X, Information Technology and Control, Vol.36, No.3, 2007, pp. 278-284.
 N. Senthilkumaran and R. Rajesh, “Edge Detection Techniques for Image Segmentation– A Survey of Soft Computing Approaches,” International Journal of Recent Trends in Engineering, Vol. 1, No. 2, May 2009.
 Sanjay Agrawal, Rutuparna Panda and Lingraj Dora, “A study on fuzzy clustering for magnetic resonance brain image segmentation using soft computing approaches,” Applied Soft Computing 24, 2014, pp. 522–533