Image Processing For Identification of Breast Cancer: A Literature SurveyAuthor : A. Arokiyamary Delphia , M. Kamarasan and S. Sathiamoorthy
Volume 7 No.2 July-December 2018 pp 28-37
Breast cancer has become the leading cause of cancer deaths among women. To decrease the related mortality, disease must be treated as early as possible, but it is hard to detect and diagnose tumors at an early stage. Manual attempt have proven to be time consuming and inefficient in many cases. Hence there is a need for efficient methods that diagnoses the cancerous cell without human involvement with high accuracy. Mammography is a special case of CT scan who adopts X-ray method & uses the high resolution film so that it can detect well the tumors in the breast. This paper reviews on the detection of the breast cancer by image processing techniques.
Breast Cancer, Image Processing, Segmentation, Pre-Processing, Mammogram, Machine Learning
 Chiranji Lal Chowdhary and D. P. Acharjya, “A Hybrid Scheme for Breast Cancer Detection using Intuitionistic Fuzzy Rough Set Technique”, International Journal of Healthcare Information Systems and Informatics, Vol. 11, No. 2, pp. 38-43, April-June 2016.
 ShikhaAgrawal, JitendraAgrawal, “Neural Network Techniques for Cancer Prediction: A Survey”, 19th International Conference on Knowledge Based and Intelligent Information and Engineering Systems, pp. 769 -774, 2015.
 S. Punitha, S. Ravi and M. Anousouya Devi, “Breast Cancer Detection in Digital Mammograms using Segmentation Techniques”, International Journal of Control Theory and Applications, Vol. 9, No. 3, pp. 167-182, 2016.
 Monica, Singh Sanjay Kumar, Agrawal Prateek and Madaan Vishu, “Breast Cancer Diagnosis using Digital Image Segmentation Techniques”, Indian Journal of Science and Technology, Vol. 9, No. 28, pp. 1-5, July 2016.
 D. Selvathi and A. AarthyPoornila, “Breast Cancer Detection In Mammogram Images Using Deep Learning Technique”, Middle-East Journal of Scientific Research, Vol. 25, No. 2, pp. 417-426, 2017.
 M. Kanchana and P. Varalakshmi, “Breast Cancer Diagnosis Using Wavelet Based Threshold Method”, Middle-East Journal of Scientific Research, Vol. 23, No. 6, pp. 1030-1034, 2015.
 S.Kasthuri, J. Lilly Pushpam, K.Mahalakshmi, Viviyenmol M D, “Segmentation of Histopathological Images using Fast Fuzzy C-Means Approach”, IJSTE – International Journal of Science Technology & Engineering, Vol. 2, No. 10, pp. 282-286, April 2016.
 Angshuman Paul and Dipti Prasad Mukherjee, “Mitosis detection for Invasive Breast Cancer Grading in Histopathological Images,” IEEE Transs. on image processing, Vol. 24, No. 11, Nov. 2015.
 Pin Wang, Xianlling Hu, Yongming Li, Qianqian Liu and Xinjian Zhu, “Automatic cell nuclei segmentation and classification of breast cancer histopathology images”, Vol. 122, May 2015.
 AnujKumar Singh and BhupendraGuptha “A Novel approaches for breast cancer cells detection and segmentation in a mammogram cells”, Vol. 54, Aug. 2015.
 Danilo Cesar Pereira, Rodrigo Pereria Ramos, Marcelo Zanchetta do Nascimento, “Segmentation and Detection of Breast Cancer cells in
mammograms combining wavelet based analysis and genetic algorithm”, Vol. 114, No. 1, April 2014.
 HumayunIrshad, SepehrJalali, Ludovic Roux, Daniel Racoceanu, Lim JooHwee, Gilles Le naour and frederiquecapron, “Automated Mitosis Detection using texture, SIFT features and HMAX biologically inspired approaches”, Vol. 4, pp. 12, March 2013.
 LuqmanMahood Mina and Nor Ashidhi Mat Isa, “A Fully Automated Breast Separation for Mammographic Images”, IEEE International Conference on Bio Signal Analysis, Processing and Systems ICBAPS, pp. 37-41, 2015.
 SemihErgin, OnurKilinc, “A new feature extraction framework based on wavelets for breast cancer diagnosis”, Computers in Biology and Medicine, Elsevier, Vol. 51, pp. 171-182, 2014.
 Any Estefany Ruiz Duque, Diana Carolina Arboleda Gómez, Jenny KateryneAristizábal Nieto, “Breast Lesions Detection in Digital Mammography:an Automated Pre-diagnosis”, IEEE conference on Signal Processing and Artificial Vision (STSIVA), pp. 1-5, 2014.
 MichielKallenberg, Kersten Petersen, Mads Nielsen andrew Y. Ng, PengfeiDiao, Christian Igel, Celine M. Vachon, Katharina Holland, RikkeRassWinkel, NicoKarssemeijer and Martin Lillholm, “Unsupervised Deep Learning Applied to Breast Density Segmentation and Mammographic Risk Scoring”, IEEE Transactions On Medical Imaging, Vol. 35, No. 5, 2016.
 JasmeenKaur, MandeepKaur, “Automatic Cancer Detection in Mammographic Images”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 5, No. 7, pp. 473-476, July 2016.
 Seemasingh and H. Sushmita, “An Efficient Neural network based system for diagnosis of Breast cancer”, BMS Institude of Technology, India, IJCSIT, Vol. 5, No. 3, pp.4354-4360, 2014.
 B. M. Gayathri, C. P. Sumathi and T. Santhanam, “Breast Cancer Diagnosis Using Machine Learning Algorithm –A Survay”, SDNB Vaishnav College for Women, Chennai, India, IJDPS, Vol. 4, No. 3, May 2013.
 Chandra PrasetyoUtomo, AanKardiana and Rika Yuliwulandari, “Breast Cancer Diagnosis using Artificial Neural Networks with Extreme Learning Techniques”, YARSI University, Jakarta, Indonesia, IJARAI, Vol. 3, No. 7, 2014.
 Minavathi, Murali.S, M.S.Dinesh, “Classification of Mass in Breast Ultrasound Images using Image Processing Techniques”, Mysore University, India, IJOCA, Vol. 42, No. 10, 2012.
 R.NITHYA, B.SANTHI, “Comparative study on feature extraction method for breast cancer classification”, School of Computing, SASTRA University, JATIT& LLS, Vol. 33, No. 2, 2005-2011.
 Ashmithakhaleel khan and noufalp, “Wavelet based automatic lesion detection using improved active contour method”, Dept. Electronics & Communication, MES College of Engineering, Kuttippuram, Malappurum, Kerala, IJERT, Vol. 3, No. 6, June 2014.
 K. Chethan and A. N. Krishna “Detection of breast masses in digital mammograms using multiple concentric layers”, Dept of CSE, SJBIT, Bangalore, IJERT, Vol. 3, No. 6, June 2014.
 Nalini Singh, Ambarish G Mohapatra, “Breast Cancer Mass Detection in Mammograms using K-means and Fuzzy C-means Clustering”, International Journal of Computer Applications, Vol. 22, No. 2, pp.15-21, May 2011.
 A.Lothe Savita, D. DeshmukhPrapti, “A survey of Image Processing techniques for Detection of Mass”, IPASJ International Journal of Computer Science (IIJCS), Vol. 2, No. 8, pp.46-51, August 2014.
 AmitChaudhary and TarunGulati “Segmenting Digital Images Using Edge Detection”, International Journal of Emerging Technology and Advanced Engineering, Vol. 3, No. 4, July 2013.
 Shanmugavadivu and Sivakumar “Wavelet Transformation-Based Detection of Masses in Digital Mammograms”, International Journal of Research in Engineering and Technology, Vol. 3, No. 2, Feb. 2014.
 PitchumaniAngayarkanni and NadiraBanu Kamal, “Mathematical Morphological Approach Mammogram Image Segmentation and Classification”, Journal of Engineering and Technology, Vol. 4, No. 3, Feb. 2014.
 Monica Jenefer and Cyrilraj, “An Efficient Image Processing Methods for Mammogram Breast Cancer Detection”, Journal of
Theoretical and Applied Information Technology, Vol. 69, Nov. 2014.
 Sutton and Bezdek, “Breast Cancer Detection Using Image Processing Techniques”, International Journal of Computer Science, 2013.
 K. Akila and P. Sumathy, “Early Breast Cancer Tumor Detection on Mammogram Images”, IJCSET, Vol. 5, No. 9, pp. 334-336, Sept. 2015.
 PratishthaShrivastava and Kirar “Detection of Tumor in mammogram images using Canny Edge Detection Technique”, International Journal of Engineering Trends and Technology, Vol. 14, Aug. 2014.
 Navjot Karur and Sanjay Singla, “A Review on Detection of Breast Cancer using Mammography”, International Journal of Innovations in Engineering and Technology, Vol. 7, No. 2, pp.173-175, Aug. 2016.
 Amandeep Singh and Amanpreetkaur, “Breast tumour detection using segmentation technique from CT scan”, IRACST – International Journal of Computer Networks and Wireless Communications, Vol. 2, No. 2, pp.187-190, April 2012.