Utilizing image processing techniques for detecting breast abnormalities in thermography images

  • Authors

    • Rabab Saadoon Abdoon babylon university
    2018-10-06
    https://doi.org/10.14419/ijet.v7i4.18312
  • Breast Abnormalities, Histogram Equalization, Segmentation, Temperature Distribution, Thermography.

  • Abstract

    Thermal Infrared (TIR) imaging of breasts involves a non-invasive, non-ionized, passive, safe and painless scan of the breasts. It is a graphing of the changes in breasts skin temperature using thermography. Thermograms are temperature distribution patterns with different colors to indicate temperature of the different regions within the tested breast, each color refers to a certain temperature range. In this work, three breast thermography images: one for normal case and two for cancerous cases, were employed to test the performance of the proposed segmentation methods: Region growing; clustering (K-means and FCM) algorithms and Histogram based enhancement technique to segment, detect and isolate the suspicious abnormal regions. These techniques were performed with the aid of suitable morphological operations to get the refined regions of interest. The results proved the efficiency of the proposed techniques to extract the abnormal (of high temperature) regions.

     

     

  • References

    1. [1] P. Kapoor, E. Bhayana and S.V.A.V Prasad. Real Time Intelligent Thermal Analysis Approach for Early Diagnosis of Breast Cancer. Vol.1, No. 5(2011)22-24.

      [2] N. Golestani, M. Etehad Tavakol, and E. Y. K. Ng, Level Set Method for Segmentation of Infrared Breast Thermograms, EXCLI Journal, Vol.13, (2014).241- 251.

      [3] N. Selvarasu, A. Nachaippan and N.M. Nandhitha. Image Processing Techniques and Neural Networks for Automated Cancer Analysis from Breast Thermographs-A Review. Indian Journal of Computer Science and Engineering, Vol. 3, No. 1 (2012) 133-137.

      [4] V. Umadevi, S.V. Raghavan and S. Jaipurkar. Framework for estimating tumour parameters using thermal imaging. Indian J Med Res 134, (2011)725-731. https://doi.org/10.4103/0971-5916.91012.

      [5] H. Q. Yang, S.-S. Xie, Q.-Y. Lin, Z. Ye, S.-Q. Chen, H. Li. A new infrared thermal imaging and its preliminary investigation of Breast Disease Assessment, Proc. Of IEEE/ICME, International Conference on Complex Medical Engineering, (2007) 1071-1074.

      [6] X. Tang, H. Ding, Y. Yuan, Q. Wang. Morphological Measurement of Localised temperature Increase Amplitudes in breast Thermograms and its clinical application. Biomedical Signal processing and Control 3(2008) 312-318. https://doi.org/10.1016/j.bspc.2008.04.001.

      [7] H. G. Zadeh, I. A. Kazerouni and J. Haddadin. Distinguish breast cancer based on thermal Features in Infrared Images, Canadian Journal on Image processing and computer vision, Vol.2, No.6 (2011) 54-58.

      [8] N. A. Mohamed. Breast cancer risk detection using digital infrared thermal images. International Journal of Bioinformatics and Biomedical Engineering, Vol. 1, No. 2 (2015) 185-194.

      [9] A. A. Al Dergazly. Thermal images segmentation for evaluation of breast cancer. International Journal of Science and Research, Vol. 6 Issue 2 (2017) 230-242.

      [10] R. S. Abdoon, L. K. Abood and S. M Ali. Adaptive techniques for brain tumor detection in MRI", Lap LAMBERT Academic Publishing, Germany (2015).

      [11] R. C. Gonzalez and R. E Woods. Digital image processing. Prentice Hall, New Jersey, (2008).

      [12] T. Y. Law and P. A. Heng. Automated extraction of bronchus from 3D CT images of lung based on genetic algorithm and 3D region growing. Medical Imaging Proc. of SPIE, Vol. 3979 (2000)906-916.

      [13] M. D. Noorul Mubarak, M. M. Sathik, Z. S. Beevi and K. Revathy. A hybrid region growing algorithm for medical image segmentation. International Journal of Computer Science & Information Technology (IJCSIT), Vol. 4, No. 3(2012) 61-70. https://doi.org/10.5121/ijcsit.2012.4306.

      [14] W. A. Abbas. CT-scan lung cancer detection using image processing techniques. Master Thesis in image processing and remote sensing, University of Baghdad, Iraq (2014).

      [15] R. S. Abdoon. Region growing and k-means hard clustering technique to extract brain abnormalities in MRI images. Journal of Babylon University/Pure and Applied Sciences, Vol. 25, No. 3, (2017)1077- 1087.

      [16] R. S. Abdoon and O. S. Khudair. Implementing segmentation techniques to isolate brain tumors in CT scan and MRI images for different orientations and modalities. Journal of Advanced Microscopy Research, Vol. 12, Issue 2(2017)116-129. https://doi.org/10.1166/jamr.2017.1329.

      [17] D. Q. Zhang, S. C. Chen, Z. S. Pan and K. R. Tan. Kernel- based fuzzy clustering incorporating spatial constraints for image segmentation. Proc. International Conference on Machine Learning and Cybernetics, Vol. 4(2003) 2189-2192.

      [18] H. P. Ng, H. H. Ong, K. W. C. Foong, P. S. Goh and W. L. Nowinski. Medical image segmentation using k-means clustering and improved watershed algorithm. IEEE Southwest Symposium on Image Analysis and Interpretation (2006) 61–65. https://doi.org/10.1109/SSIAI.2006.1633722.

      [19] Y. Yang and S. Huang. Image segmentation by fuzzy c-means clustering algorithm with a novel penalty term. Computing and Informatics, Vol. 26 (2007) 17-31.

      [20] K. S. Angel Viji and J. Jayakumari. Performance evaluation of standard image segmentation methods and clustering algorithms for segmentation of MRI brain tumor images. European Journal of Scientific Research, Vol. 79, No. 2 (2012) 166-179.

      [21] P. Dhanalakshmi and T. Kanimozhi. Automatic segmentation of brain tumor using k-means clustering and its area calculation. International Journal of Advanced Electrical and Electronics Engineering, Vol. 2, Issue 2 (2013) 130-134.

      [22] R. C Gonzalez, R. E. Woods and S. L. Eddins. Digital image processing using MATLAB. Pearson Prentice-Hall (2004).

      [23] American College of Clinical Thermology, available online at: http://www.thermologyonline.org/Breast/breast_thermography_what.htm, accessed July 3, 2018.

      [24] Available online at Website: http://www.breastthermography.com/case_studies.htm, accessed July 3, 2018.

  • Downloads

  • How to Cite

    Saadoon Abdoon, R. (2018). Utilizing image processing techniques for detecting breast abnormalities in thermography images. International Journal of Engineering & Technology, 7(4), 2810-2817. https://doi.org/10.14419/ijet.v7i4.18312

    Received date: 2018-08-26

    Accepted date: 2018-09-18

    Published date: 2018-10-06