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An Evaluation of Edge Detection Algorithms for Mammographic Calcifications

  • Vikrant Bhateja
  • Swapna Devi
  • Shabana Urooj
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 222)

Abstract

Edge detection is an important module in medical imaging for diagnostic detection and extraction of features. The main limitation of the existing evaluation measures for edge detection algorithms is the requirement of a reference image for comparison. Thus, it becomes difficult to assess the performance of edge detection algorithms in case of mammographic features. This paper presents a new version of reconstruction estimation function for objective evaluation of edge enhanced mammograms containing microcalcifications. It is a non-reference approach helpful in selection of most appropriate algorithm for edge enhancement of microcalcifications and also plays a key role in selecting parameters for performance optimization of these algorithms. Simulations are performed on mammograms from MIAS database with different category of background tissues; the obtained results validate the efficiency of the proposed measure in precise assessment of mammograms (edge-maps) in accordance with the subjectivity of human evaluation.

Keywords

Edge detection MIAS Microcalcifications Non-reference Reconstruction estimation function 

References

  1. 1.
    Rovere GQ, Warren R, Benson JR (2006) Early breast cancer from screening to multidisciplinary management, 2nd edn. Taylor and Francis Group, FloridaGoogle Scholar
  2. 2.
    Canny JA (1986) Computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 6:679–698 (PAMI-8)Google Scholar
  3. 3.
    Woodhall M, Linquist C (1997) New edge detection algorithms based on adaptive estimation filters. In: 31st asilomar IEEE conference on signals systems & computers, vol 2. pp 1695–1699Google Scholar
  4. 4.
    Sharifi M., Fathy M, Mahmoudi MT (2002) A classified and comparative study of edge detection algorithms. In: Proceedings of the international conference on information technology: coding and computing (ITCC.02), (2002)Google Scholar
  5. 5.
    Strickland RN, Hahn H (1996) Wavelet transform for detecting microcalcifications in mammograms. IEEE Trans Med Imaging 15(2):218–229CrossRefGoogle Scholar
  6. 6.
    Dominguez JQ, Cortina-Januchs MG, Jevti’c A, Andina D, Barron-Adame JM, Vega-Corona A (2009) Combination of nonlinear filters and ANN for detection of microcalcifications in digitized mammography. In: Proceedings of the international conference on systems, man, and cybernetics, USA, pp 1516–1520Google Scholar
  7. 7.
    Papadopoulos A, Fotiadis DI, Costaridou L (2008) Improvement of microcalcification cluster detection in mammography utilizing image enhancement techniques. Comput Biol Med 38(10):1045–1055CrossRefGoogle Scholar
  8. 8.
    Wu Z, Yuan J, LV B, Zheng X (2010) Digital mammography image enhancement using improved unsharp masking approach. In: Proceedings of 3rd international congress on image and signal processing (CISP), vol 2. pp 668–672Google Scholar
  9. 9.
    Polosel A, Ramponi G, Mathews VJ (2000) Image enhancement via adaptive unsharp masking. IEEE Trans Image Process 9:505–510CrossRefGoogle Scholar
  10. 10.
    Yang YB, Shang HB, Jia GC, Huang LQ (2003) Adaptive unsharp masking method based on region segmentation. Optics Precis Eng 11:188–191Google Scholar
  11. 11.
    Stojic T, Reljin I, Reljin B (2005) Local contrast enhancement in digital mammography by using mathematical morphology. In: International symposium on signals, circuits and systems (ISSCS), vol 2. pp 609–612Google Scholar
  12. 12.
    Stojic T, Reljin B (2010) Enhancement of microcalcifications in digitized mammograms: multifractal and mathematical morphology approach. FME Trans 38(1):1–9Google Scholar
  13. 13.
    Dominguez JQ, Sanchez-Garcia M, Gozalez-Romo M, Vega-Corona A, Andina D (2009) Feature extraction using coordinate logic filters and artificial neural networks. In: 7th international conference on industrial informatics (INDIN’09), pp 645–649Google Scholar
  14. 14.
    Santhaiah Ch, Babu GA, Rani MU (2009) Gray-level morphological operations for image segmentation and tracking edges on medical applications. Int J Comput Sci Network Secur 9(7):131–136Google Scholar
  15. 15.
    Bhateja V, Devi S (2011) A novel framework for edge detection of microcalcifications using a non-linear enhancement operator and morphological filter. In: Proceedings of 3rd international conference on electronics & computer technology (ICECT-2011), Kanyakumari (India), vol 5. pp 419–424Google Scholar
  16. 16.
    Shin M, Goldgof D, Bowyer K, Nikiforou S (2001) Comparison of edge detection algorithms using a structure from motion task. IEEE Trans Syst Man Cybern B 31(4):589–601CrossRefGoogle Scholar
  17. 17.
    Yitzhaky Y, Peli E (2003) A method for objective edge detection evaluation and detector parameter selection. IEEE Trans Pattern Anal Mach Intell 25(8):1027–1033CrossRefGoogle Scholar
  18. 18.
    Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612CrossRefGoogle Scholar
  19. 19.
    Trahanias P, Venetsanopoulos A (1996) Vector order statistics operators as color edge detectors. IEEE Trans Syst Man Cybern B 26(1):135–143CrossRefGoogle Scholar
  20. 20.
    Pratt W (1991) Digital image processing, 2nd edn. Wiley, New YorkMATHGoogle Scholar
  21. 21.
    Yitzhaky Y, Peli E (2003) A method for objective edge detection evaluation and detector parameter selection. IEEE Trans Pattern Anal Mach Intell 25(8):1027–1033CrossRefGoogle Scholar
  22. 22.
    Devi S, Soni A, Thakur N, Das M (2010) Image quality metric for coupled noise and distortion. In: 4th international conference on advanced computing and communication technologies (ICACCT-2010), IndiaGoogle Scholar
  23. 23.
    Carlsson S (1988) Sketch based coding of gray level images. Signal Process 15(1):57–83CrossRefGoogle Scholar
  24. 24.
    Elder J (1999) Are edges incomplete? Int J Comp Vis 34(2/3):97–122Google Scholar
  25. 25.
    Govindarajan B, Panetta K, Agaian S (2008) Image reconstruction for quality assesment of edge detectors. In: Proceedings of IEEE international conference on systems, man, and cybernetics, pp 691–696Google Scholar
  26. 26.
    Govindarajan B, Panetta K, Agaian S (2009) A non-reference measure for objective edge map evaluation. In: Proceedings of IEEE international conference on systems, man, and cybernetics, pp 4563–4568Google Scholar
  27. 27.
    Carlsson S (1988) Sketch based coding of gray level images. Signal Process 15(1):57–83CrossRefGoogle Scholar
  28. 28.
    Suckling J, et al. (1994) The mammographic image analysis society mammogram database. In: Proceedings of 2nd international workshop digital mammography,U.K, pp 375–378Google Scholar

Copyright information

© Springer India 2013

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringSRMGPCLucknowIndia
  2. 2.Department of Electronics and Communication EngineeringNITTTRChandigarhIndia
  3. 3.Electrical Engineering Department, School of EngineeringGBUGr. NoidaIndia

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