Tumor Mass Identification Based on Surface Analysis and Fractal Dimensions

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 150)

Abstract

In present paper we have utilized wavelet transform and fractal dimensions to analyze tumor mass for breast cancer screening using mammogram. Boundary based features from shape of the tumor have taken into consideration as these represent one of the very important property for tumor mass analysis. In present work surface analysis using imaging of tumor mass for analysis of the lesions has been accomplished.

Keywords

Wavelet Fractal dimenisions Sufrace analysis Mammogram Boundary feature 

References

  1. 1.
    Bhattacharya M, Das A (2009) Soft computing based decision making approach for tumor mass identification in mammogram. Int J Bioinformatics Res 1(2):37–46. ISSN: 0975–3087Google Scholar
  2. 2.
    Sankar D, Thomas T (2009) Analysis of mammograms using fractal features. India nature & biologically inspired computing, 2009. NaBIC 2009. World congress on issue date: 9–11 Dec 2009, pp 936–941Google Scholar
  3. 3.
    Saritha S, Santhosh Kumar G (2011) Interestingness analysis of semantic association mining in medical images. Commun Comput Inf Sci 157:1–10Google Scholar
  4. 4.
    Qin B, Ma L, Xu W (2010) Comparative study on boundary structural irregularity using local FD and curvature analysis. Bioinformatics and Biomedical Engineering (iCBBE), 2010 4th international conference on 18–20 June 2010, pp 1–4Google Scholar
  5. 5.
    Mu T, Nandi AK, Rangayyan RM (2008) Classification of breast masses using selected shape, edge-sharpness, and texture features with linear and kernel-based classifiers. J Digit Imaging 21(2):153–169Google Scholar
  6. 6.
    Jampala S (1992) Fractals: classification, generation and applications. Circuits and systems, 1992, Proceedings of the 35th Midwest symposium, pp 1024–1027Google Scholar
  7. 7.
    Iftekharuddin KM, Jia W, Marsh R (2003) Fractal analysis of tumor in brain MR images. Machine vision and applications. Springer, Heidelberg, pp 352–362Google Scholar
  8. 8.
    Bhattacharya M, Das A (2010) Identification of tiny and large calcification in breast: a study on mammographic image analysis. Int J Bioinformatics Res Appl 6(4):418–434Google Scholar
  9. 9.
    Zuo Y, Lin J, Chen K, Peng Y (2009) Boundary-based feature extraction and recognition of breast tumors using support vector machine. 2009 International forum on information technology and application, pp 89–92Google Scholar
  10. 10.
    Mavroforakis ME, Georgiou HV, Dimitropoulos N, Cavouras D, Theodoridis S (2006) Mammographic masses characterization based on localized texture and dataset fractal analysis using linear, neural and support vector machine classifiers. Artif Intell Med 37:145–162Google Scholar
  11. 11.
    Hirano S, Tsumoto S (2008) A method for detecting suspicious regions in mammograms based on multiscale image filtering and regression-line analysis. Automation congress, 2008. WAC 2008. World 09 Dec 2008, pp 1–6Google Scholar
  12. 12.
    Rangayyan RM, El-faramawy NM, Leo Desautels JE, Alim OA (1997) Measures of acutance and shape for classification of breast tumors. IEEE Trans Med Imaging 16(6):799–810Google Scholar
  13. 13.
    Tourassi GD, Eltonsy NH, Graham JH, Floyd CE, Elmaghraby AS (2005) Feature and knowledge based analysis for reduction of false positives in the computerized detection of masses in screening mammography. 2005 IEEE engineering in medicine and biology 27th annual conference Shanghai, China, 1–4 Sept 2005, pp 6524–6527Google Scholar
  14. 14.
    Chung D, Revathy K, Choi E, Min D (2009) A neural network approach to mammogram image classification using fractal features. Intelligent computing and intelligent systems, 2009. ICIS 2009, pp 444–447Google Scholar
  15. 15.
  16. 16.
    Lin Y, Xiao XR, Li XP, Zhou XW (2005) Wavelet analysis of the surface morphologic of nanocrystalline TiO2 thin films. Surf Sci 579:37–46Google Scholar
  17. 17.
    Mallat SG (1989) A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Mach Intell 11(5):674–693Google Scholar
  18. 18.
    Borodich FM, Onishchenko DA (1999) Similarity and fractality in the modelling of roughness by a multilevel profile with hierarchical structure. Int J Solids Struct 36:2585–2612Google Scholar
  19. 19.
    Dubuc B, Quiniou J, Roques-Carmes C, Tricot C, Zucker SW (1989) Evaluating the fractal dimension of profiles. Phys Rev A 39:1500–1512Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Department of Information TechnologyIndian Institute of Information Technology and ManagementGwaliorIndia

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