Performance of segmentation in infrared breast thermograms using level set method

  • Authors

    • S Saran Raj
    • Hariharan R
    https://doi.org/10.14419/ijet.v7i1.7.10642

    Received date: March 26, 2018

    Accepted date: March 26, 2018

    Published date: February 5, 2018

  • Breast Thermograohy, Level Set, K-Means

  • Abstract

    Breast cancer is the overall most basic obtrusive tumour in females. The breast cancer can be dealt with successfully that they are analyzed at a beginning time. It can watch the principal indication of shaping up disease before mammography can recognize. The thermal data can be appeared in a pseudo shading where each shading speaks to a particular scope of temperature. Different techniques can be connected to extricate hot districts for distinguishing associated areas with interests in the thermograms and possibly suspicious tissues. in this paper at first the pre process of the thermogram pictures are done then they are improved. The upgraded pictures are divided by two picture segmentation strategy: K-means and level set technique are study and compared. The highlights have been extracted and classification for both the segmentation techniques.

  • References

    1. Moon WK, Shen YW, Bae MS, “Computer Aided Tumor De-tection Based On Multi Scale Blob Detection Algorithm In Auto-mated Breast Ultrasound Pictures”,IEEE Transactions On Medi-cal Imaging, 2013;32(7).
    2. Chandhok C, Chathurvedi S, A. Khurshid AA, “An approach to picture segmentation using K-means clustering algorithm”, In-ternational Journal of Information Technology(IJIT),2012; 1(1).
    3. Goyal G, Singhal M, Bansal A, “Comparison of denoising filters on colour TEM picture for different noise”, International Journal of Picture Processing and Vision Sciences(IJIPVS) ISSN:2278-1110,2012; 1(3).
    4. Manoj R and Thamarai M, “A Survey of segmentation in mass de-tection algorithm for mammography and thermography”, ISSN (Print): 2278-8948, 2012;1(2).
    5. Manikandaprabu N, Lalli G, Marimuthu CN, “A Development of knowledge based inferences system for detection of breast cancer on thermogram pictures”, International conference on computer communication and informatics (ICCCI -2014), 2014;03-05.
    6. Ganeshan P, Chakravarthy P, Shweta Verma S, Segmentation of natural colour pictures in HSI color space based on FCM cluster-ing”, International Journal of Advanced Research In Computer Engineering and Technology, (IJARCET), 2013; 3: 3.
    7. Kapoor PP, Prasad AV, Patni S, “Picture segmentation and asymmetry analysis of breast thermograms for tumor detection”, International Journal of Computer Applications (0975 – 8887), 2012 50(9).
    8. Kapoor PP, Prasad AV, Patni S, “Automatic analysis of breast thermograms for tumour detection based bio statistical feature ex-traction and ANN”, International Journal of Emerging Trends in Engineering and Development, 2012; l7(2).
    9. Suganya R, Shanthi R,”Fuzzy C means algorithm- A Review”, International Journal of Scientific and Research Publications, 2012 ;2(11).
    10. NG EYK, Chen Y. “Segmentation of breast thermogram: im-proved boundary detection with modified snake algorithm”. J Mech Med Biol 2006; 6:123-36.
    11. Ng EYK, Kee EC. “Advanced integrated technique in breast can-der thermography”. J Med EngTechnol 2008;32:103-14.
    12. Osher S, Fedkiw R. “Level set methods and dynamic implicit sur-faces”. New York: Springer, 2003.
    13. Sethian JA. “A fast marching level set method for monotonically advancing fronts”. ProcNatlAcadSci USA 1996;93:1591-5. EX-CLI Journal 2014;13:241-251 – ISSN 1611-2156 Received: Jan-uary 11, 2014, accepted: February 07, 2014, published: March 12, 2014 251.

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  • How to Cite

    Saran Raj, S., & R, H. (2018). Performance of segmentation in infrared breast thermograms using level set method. International Journal of Engineering and Technology, 7(1.7), 161-164. https://doi.org/10.14419/ijet.v7i1.7.10642