Journal of Digital Imaging

, Volume 21, Issue 2, pp 129–144 | Cite as

Feature Extraction from a Signature Based on the Turning Angle Function for the Classification of Breast Tumors

  • Denise Guliato
  • Juliano D. de Carvalho
  • Rangaraj M. Rangayyan
  • Sérgio A. Santiago
Article

Abstract

Malignant breast tumors and benign masses appear in mammograms with different shape characteristics: the former usually have rough, spiculated, or microlobulated contours, whereas the latter commonly have smooth, round, oval, or macrolobulated contours. Features that characterize shape roughness and complexity can assist in distinguishing between malignant tumors and benign masses. Signatures of contours may be used to analyze their shapes. We propose to use a signature based on the turning angle function of contours of breast masses to derive features that capture the characteristics of shape roughness as described above. We propose methods to derive an index of the presence of convex regions (XRTA), an index of the presence of concave regions (VRTA), an index of convexity (CXTA), and two measures of fractal dimension (FDTA and FDdTA) from the turning angle function. The methods were tested with a set of 111 contours of 65 benign masses and 46 malignant tumors with different parameters. The best classification accuracies in discriminating between benign masses and malignant tumors, obtained for XRTA, VRTA, CXTA, FDTA, and FDdTA in terms of the area under the receiver operating characteristics curve, were 0.92, 0.92, 0.93, 0.93, and, 0.92, respectively.

Key words

Breast cancer tumor classification fractal dimension index of convexity index of concavity shape features turning angle function 

References

  1. 1.
    Homer MJ: Mammographic Interpretation: A Practical Approach, 2nd edition. Boston, MA: McGraw-Hill, 1997Google Scholar
  2. 2.
    American College of Radiology: Breast Imaging Reporting and Data System BI-RADS, 4th edition. Reston, VA: American College of Radiology, 2004Google Scholar
  3. 3.
    Rangayyan RM, El-Faramawy NM, Desautels JEL, Alim OA: Measures of acutance and shape for classification of breast tumors. IEEE Trans Med Imaging 16(6):799–810, 1997PubMedCrossRefGoogle Scholar
  4. 4.
    Rangayyan RM, Mudigonda NR, Desautels JEL: Boundary modelling and shape analysis methods for classification of mammographic masses. Med Biol Eng Comput 38:487–496, 2000PubMedCrossRefGoogle Scholar
  5. 5.
    Alto H, Rangayyan RM, Desautels JEL: Content-based retrieval and analysis of mammographic masses. J Electron Imaging 14(2):023016:1–17, 2005Google Scholar
  6. 6.
    Bruce LM, Adhami RR: Classifying mammographic mass shapes using the wavelet transform modulus-maxima method. IEEE Trans Med Imaging 18(12):1170–1177, 1999PubMedCrossRefGoogle Scholar
  7. 7.
    Sahiner BS, Chan HP, Petrick N, Helvie MA, Hadjiiski LM: Improvement of mammographic mass characterization using spiculation measures and morphological features. Med Phys 28(7):1455–1465, 2001PubMedCrossRefGoogle Scholar
  8. 8.
    Rangayyan RM, Nguyen TM: Fractal analysis of contours of breast masses in mammograms. J Digital Imaging, 2007, in press 10.1007/s10278-006-0860-9
  9. 9.
    Huo Z, Giger ML, Vyborny CJ, Wolverton DE, Metz CE: Computerized classification of benign and malignant masses on digitized mammograms: A study of robustness. Acad Radiol 7(12):1077–1084, 2000PubMedCrossRefGoogle Scholar
  10. 10.
    Huo Z, Giger ML, Vyborny CJ: Computerized analysis of multiple-mammographic views: Potential usefulness of special view mammograms in computer-aided diagnosis. IEEE Trans Med Imaging 20(12):1285–1292, 2001PubMedCrossRefGoogle Scholar
  11. 11.
    Nandi RJ, Nandi AK, Rangayyan RM, Scutt D: Classification of breast masses in mammograms using genetic programming and feature selection. Med Biol Eng Comput 44(8):683–694, August 2006PubMedCrossRefGoogle Scholar
  12. 12.
    Mudigonda NR, Rangayyan RM, Desautels JEL: Detection of breast masses in mammograms by density slicing and texture flow-field analysis. IEEE Trans Med Imaging 20(12):1215–1227, 2001PubMedCrossRefGoogle Scholar
  13. 13.
    Wei D, Chan HP, Helvie MA, Sahiner B, Petrick N, Adler DD, Goodsitt MM: Classification of mass and normal breast tissue on digital mammograms: multiresolution texture analysis. Med Phys 22(9):1501–1513, 1995PubMedCrossRefGoogle Scholar
  14. 14.
    Rangayyan RM, Guliato D, Carvalho JD, Santiago SA: Feature extraction from the turning angle function for the classification of breast tumors. In Proceedings of the International Special Topics Conference on Information Technology in Biomedicine -IEEE ITAB2006, Ioannina, Greece, October 2006, 6 pages on CDROMGoogle Scholar
  15. 15.
    Carvalho JD, Rangayyan RM, Guliato D, Santiago SA: Polygonal modeling of contours using the turning angle function. 20th IEEE Canadian Conference on Electrical and Computer Engineering, Vancouver, BC, April 2007, 4 pages on CDROMGoogle Scholar
  16. 16.
    Guliato D, Rangayyan RM, Carvalho JD, Santiago SA: Spiculation-preserving polygonal modeling of contours of breast tumors. Proceedings of the 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pages 2791–2794, New York City, NY, September 2006Google Scholar
  17. 17.
    Guliato D, Rangayyan RM, Carvalho JD, Santiago SA: Polygonal modeling of contours with the preservation of spicules. IEEE Trans Biomed Eng, 2007, in pressGoogle Scholar
  18. 18.
    Rangayyan RM: Biomedical Image Analysis. Boca Raton, FL: CRC, 2005Google Scholar
  19. 19.
    Pohlman S, Powell KA, Obuchowski NA, Chilcote WA, Grundfest-Broniatowski S: Quantitative classification of breast tumors in digitized mammograms. Med Phys 23(8):1337–1345, 1996PubMedCrossRefGoogle Scholar
  20. 20.
    Niblack W, Yin J: A pseudo-distance measure for 2D shapes based on turning angle. IEEE International Conference on Image Processing, volume 3, pages 352–355, Washington DC, October 1995Google Scholar
  21. 21.
    Veltkamp RC, Tanase M: Part-based shape retrieval. Proceedings of the 13th Annual ACM International Conference on Multimedia, pages 543–546, Singapore, November 2005Google Scholar
  22. 22.
    Veltkamp RC, Tanase M, Sent D: Features in content-based image retrieval systems: a survey. In State-of-the-Art in Content-Based Image and Video Retrieval [Dagstuhl Seminar, 5–10 December 1999], pages 97–124, Deventer, The Netherlands: Kluwer, B.V., 2001Google Scholar
  23. 23.
    Veltkamp RC: Shape matching: Similarity measures and algorithm. IEEE SMI 2001 International Conference on Shape Modeling and Applications, pages 188–197, Genova, Italy, May 2001Google Scholar
  24. 24.
    Veltkamp RC: Shape algorithmics. Algorithmica 38(1):1–4, 2003CrossRefGoogle Scholar
  25. 25.
    Latecki LJ, Lakämper R: Application of planar shape comparisons to object retrieval in image databases. Pattern Recogn 35(1):15–29, 2002CrossRefGoogle Scholar
  26. 26.
    Latecki LJ, Lakämper R, Wolter D: Shape similarity and visual parts. International Conference on Discrete Geometry for Computer Imagery (DGCI), pages 34–51, Napoles, Italy, 2003Google Scholar
  27. 27.
    Arkin EM, Chew LP, Huttenlocher DP, Kedem K, Mitchell JSB: An efficiently computable metric for comparing polygonal shapes. IEEE Trans Pattern Anal Mach Intell 13:209–216, March 1991CrossRefGoogle Scholar
  28. 28.
    Mandelbrot BB: The Fractal Geometry of Nature. San Francisco, CA: Freeman, 1983Google Scholar
  29. 29.
    Peitgen HO, Jürgens H, Saupe D: Chaos and Fractals: New Frontiers of Science. New York, NY: Springer, 2004Google Scholar
  30. 30.
    Deering W, West BJ: Fractal physiology. IEEE Eng Med Biol Mag 11(2):40–46, June 1992CrossRefGoogle Scholar
  31. 31.
    Schepers HE, van Beek JHGM, Bassingthwaighte JB: Four methods to estimate the fractal dimension from self-affine signals. IEEE Eng Med Biol Mag 11(2):57–64, June 1992CrossRefGoogle Scholar
  32. 32.
    Fortin C, Kumaresan R, Ohley W, Hoefer S: Fractal dimension in the analysis of medical images. IEEE Eng Med Biol Mag 11(2):65–71, June 1992CrossRefGoogle Scholar
  33. 33.
    Goldberger AL, Rigney DR, West BJ: Chaos and fractals in human physiology. Sci Am 262:42–49, February 1990PubMedCrossRefGoogle Scholar
  34. 34.
    Matsubara T, Fujita H, Kasai S, Goto M, Tani Y, Hara T, Endo T: Development of new schemes for detection and analysis of mammographic masses. In Proceedings of the 1997 IASTED International Conference on Intelligent Information Systems (IIS ’97), pages 63–66, Grand Bahama Island, Bahamas, December 1997Google Scholar
  35. 35.
    Screen Test: Alberta Program for the Early Detection of Breast Cancer—2001/03 Biennial Report. http://www.cancerboard.ab.ca/screentest, 2004
  36. 36.
    Alto H, Rangayyan RM, Paranjape RB, Desautels JEL, Bryant H: An indexed atlas of digital mammograms for computer-aided diagnosis of breast cancer. Annales des T’el’ecommunications 58(5–6):820–835, 2003Google Scholar
  37. 37.
    The Mammographic Image Analysis Society digital mammogram database. http://peipa.essex.ac.uk/info/mias.html, accessed October 2006
  38. 38.
    Suckling J, Parker J, Dance DR, Astley S, Hutt I, Boggis CRM, Ricketts I, Stamatakis E, Cerneaz N, Kok SL, Taylor P, Betal D, Savage J: The Mammographic Image Analysis Society digital mammogram database. In Gale AG, Astley SM, Dance DR, and Cairns AY, editors, Proceedings of the 2nd International Workshop on Digital Mammography, volume 1069 of Excerpta Medica International Congress Series, pages 375–378, York, UK, July 1994Google Scholar
  39. 39.
    Metz CE: Basic principles of ROC analysis. Semin Nucl Med VIII(4):283–298, 1978CrossRefGoogle Scholar

Copyright information

© Society for Imaging Informatics in Medicine 2007

Authors and Affiliations

  • Denise Guliato
    • 1
  • Juliano D. de Carvalho
    • 1
  • Rangaraj M. Rangayyan
    • 2
  • Sérgio A. Santiago
    • 1
  1. 1.Faculdade de ComputaçãoUniversidade Federal de UberlândiaMinas GeraisBrazil
  2. 2.Department of Electrical and Computer EngineeringUniversity of Calgary Schulich School of EngineeringCalgaryCanada

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