Automatic Diagnosis of Breast Cancer using Thermographic Color Analysis and SVM Classifier

  • Asmita T. WakankarEmail author
  • G. R. Suresh
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 530)


Breast cancer is the commonly found cancer in women. Studies show that the detection at the earliest can bring down the mortality rate. Infrared Breast thermography uses the temperature changes in breast to arrive at diagnosis. Due to increased cell activity, the tumor and the surrounding areas has higher temperature emitting higher infrared radiations. These radiations are captured by thermal camera and indicated in pseudo colored image. Each colour of thermogram is related to specific range of temperature. The breast thermogram interpretation is primarily based on colour analysis and asymmetry analysis of thermograms visually and subjectively. This study presents analysis of breast thermograms based on segmentation of region of interest which is extracted as hot region followed by colour analysis. The area and contours of the hottest regions in the breast images are used to indicate abnormalities. These features are further given to ANN classifier for automated analysis. The results are compared with doctor’s diagnosis to confirm that infra-red thermography is a reliable diagnostic tool in breast cancer identification.


Breast cancer Thermography Segmentation Level Set ANN 


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  1. 1.
    Ganesh Sharma, Rahul Dave, Jyostna Sanadya, Piyush Sharma and K.K.Sharma: Various Types and management of Breast Cancer: An Overview, Journal of Advanced Pharmaceutical Technology and Research, pp 109-126,1(2) (2010)Google Scholar
  2. 2.
    Shahari Sheetal, Asmita Wakankar: Color Analysis of thermograms for Breast Cancer Detection: Proceedings IEEE International Conference on Industrial Instrumentation and Control (2015)Google Scholar
  3. 3.
    Harold H. Szu, Charles Hsu, Philip Hoekstra, Jerry Beeney: Biomedical Wellness Standoff Screening by Unsupervised Learning: Proceedings SPIE 7343, Independent Component Analyses, Wavelets, Neural Networks, Biosystems and Nanoengineering VII, 734319 (2009)Google Scholar
  4. 4.
    Hossein Zadeh, Imran Kazerouni, Javad Haddaadnia: Diagnosis of Breast Cancer and Clustering Technique using Thermal Indicators Exposed to Infrared Images, Journal of American Science, Vol 7(9), pp 281-288 (2011)Google Scholar
  5. 5.
    Pragati Kapoor, Seema Patni, Dr. S.V.A.V Prasad: Image Segmentation and Asymmetry Analysis of Breast Thermograms for Tumor Detection, International Journal of Computer Applications, Volume 50, No 9 (2012)Google Scholar
  6. 6.
    Asmita Wakankar, G. R. Suresh, Akshata Ghugare: Automatic Diagnosis of Breast Abnormality Using Digital IR Camera, IEEE International Conference on Electronic Systems, Signal Processing and Computing Technologies (2014)Google Scholar
  7. 7.
    Saad N.H.,N.Mohonma Sabri,A.F.Hasa,Azuwa Ali, Hariyanti Mahd Saleh: Defect Segmentation of Semiconductor Wafer Image Using K Means Clustering, Applied Mechanics & Materials, Vol 815, pp 374-379 (2015)Google Scholar
  8. 8.
    Anubha, R.B. Dubey: A Review on MRI Image Segmentation Techniques, International Journal of Advanced Research in Electronics and Communication Engineering, Vol 4, Issue 5 (2015)Google Scholar
  9. 9.
    N. Golestani, M. Tavakol, E.Y.K.Ng: Level set Method for Segmentation of Infrared Breast Thermograms, EXCLI Journal, pp 241-251(2014)Google Scholar
  10. 10.
    Chuming Li, Rui Huang et al: A Level Set Method for Image Segmentation in the Presence of Intensity Inhomogeneities with Application to MRI, IEEE Transactions in Image Processing, Vol 20, No 7 (2011)Google Scholar
  11. 11.
    U. Rajendra Acharya, E.Y.K.Ng, Jen Hong Tan, S. Vinitha Sree: Thermography based Breast Cancer Detection Using Texture Features and Support Vector Machine, Journal of Medical Systems, Vol 36, pp 1503-1510 (2012)Google Scholar
  12. 12.
    Kieran Jay Edwards, Mahamed Gaber: Adopted Data Mining Methods, Studies in Big Data, pp 31-42 (2014)Google Scholar
  13. 13.
    Nijad Al Najdawi, Mariam Biltawi, Sara Tedmori: Mammogram Image Visual Enhancement, mass segmentation & Classification, Applied Soft Computing, Vol 35, pp 175-185 (2015)Google Scholar
  14. 14.
    A. Lavanya: An Approach to Identify Lesion in Infrared Breast Thermography Images using Segmentation and Fractal Analysis, International Journal of Biomedical Engineering and Technology, Vol 19, No 3 (2015)Google Scholar
  15. 15.
    S.S.Suganthi, S.Ramakrishnan: Anisotropic Diffusion Filter Based Edge Enhancement for Segmentation of Breast Thermogram Using Level sets, Elsevier, Biomedical Signal Processing and Control, Vol 10 (2014)Google Scholar
  16. 16.
    Prabha S, Anandh K.R, Sujatha C.M, S.Ramakrishnan: Total Variation Based Edge Enhancement for Levelset Segmentation and Asymmetry Analysis in Breast Thermograms, IEEE, pp 6438- 6441 (2014)Google Scholar
  17. 17.
    S.S.Srinivasan, R. Swaminathan: Segmentation of Breast Tissues in Infrared Images Using Modified Phase Based Level Sets, Biomedical Informatics and Technology, Vol 404, pp 161-174 (2014)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Sathyabama UniversityChennaiIndia
  2. 2.Easwari Engineering CollegeChennaiIndia

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