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Improving class separability using extended pixel planes: a comparative study

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Abstract

In this work we explored class separability in feature spaces built on extended representations of pixel planes (EPP) produced using scale pyramid, subband pyramid, and image transforms. The image transforms included Chebyshev, Fourier, wavelets, gradient, and Laplacian; we also utilized transform combinations, including Fourier, Chebyshev, and wavelets of the gradient transform, as well as Fourier of the Laplacian transform. We demonstrate that all three types of EPP promote class separation. We also explored the effect of EPP on suboptimal feature libraries, using only textural features in one case and only Haralick features in another. The effect of EPP was especially clear for these suboptimal libraries, where the transform-based representations were found to increase separability to a greater extent than scale or subband pyramids. EPP can be particularly useful in new applications where optimal features have not yet been developed.

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References

  1. Tanimoto S., Pavlidis T.: A hierarchical data structure for picture processing. Comput. Graph. Image Process. 4, 104–119 (1975)

    Article  Google Scholar 

  2. Bourbakis N.G., Klinger A.: A hierarchical picture coding scheme. Pattern Recognit. 22, 317–329 (1989)

    Article  Google Scholar 

  3. Burt P., Adelson E.: The Laplacian pyramid as a compact image code. IEEE Trans. Commun. 31, 532–540 (1983)

    Article  Google Scholar 

  4. Simoncelli E., Adelson E.H.: Subband transforms. In: Woods, J. (eds) Subband Image Coding, Kluwer, Norwell (1991)

    Google Scholar 

  5. Lindeberg T.: Scale-Space Theory in Computer Vision. Kluwer, Norwell (1994)

    Google Scholar 

  6. Freeman W.H., Adelson E.H.: The design and use of steerable filters. IEEE Trans. Pattern Anal. Mach. Intell. 13, 891–906 (1991)

    Article  Google Scholar 

  7. Lowe D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60, 91–110 (2004)

    Article  Google Scholar 

  8. Rosenfeld A.: From image analysis to computer vision: an annotated bibliography 1955–1979. Comput. Vis. Image Underst. 84, 298–324 (2001)

    Article  MATH  Google Scholar 

  9. Murphy, K., Torrralba, A., Eaton, D., Freeman, W.: Object detection and localization using local and global featues. In: Lecture Notes in Computer Science, vol. 4170, pp. 382–400 (2006)

  10. Boland M.V., Murphy R.F.: A neural network classifier capable of recognizing the patterns of all major subcellular structures in fluorescence microscope images of HeLa cells. Bioinformatics 17, 1213–1223 (2001)

    Article  Google Scholar 

  11. Murphy R.F.: Automated interpretation of protein subcellular location patterns: implications for early detection and assessment. Ann. N. Y. Acad. Sci. 1020, 124–131 (2004)

    Article  Google Scholar 

  12. Ranzato M., Taylor P.E., House J.M., Flagan R.C., LeCun Y., Perona P.: Automatic recognition of biological particles in microscopic images. Pattern Recognit. Lett. 28, 31–39 (2007)

    Article  Google Scholar 

  13. Orlov, N., Johnston, J., Macura, T., Wolkow, C., Goldberg, I.: Pattern recognition approaches to compute image similarities: application to age related morphological change. In: International Symposium on Biomedical Imaging: From Nano to Macro, Arlington, VA, pp. 1152–1156 (2006)

  14. Johnston J.L., Iser W.B., Chow D.K., Goldberg I.G., Wolkow C.A.: Quantitative image analysis reveals distinct structural transitions during aging in Caenorhabditis elegans tissues. PLOS One 3, e2821 (2008)

    Article  Google Scholar 

  15. Orlov N.V., Chen W.W., Eckley D.M., Macura T.J., Shamir L., Jaffe E.S., Goldberg I.G.: Automatic classification of lymphoma images with transform-based global features. IEEE Trans. Inf. Technol. Biomed. 14, 1003–1013 (2010)

    Article  Google Scholar 

  16. Orlov, N.V., Eckley, D.M., Shamir, L., Goldberg, I.G.: Machine vision for classifying biological and biomedical images. In: Visualization, Imaging and Image Processing. Palma de Mallorca, Spain (2008)

  17. Shamir L., Ling S.M., Scott W., Orlov N., Macura T., Eckley D.M., Ferrucci L., Goldberg I. G.: Knee X-ray image analysis method for automated detection of Osteoarthritis. IEEE Trans. Biomed. Eng. 56, 407–415 (2009)

    Article  Google Scholar 

  18. Shamir, L., Ling, S.M., Scott, W., Hochberg, M., Ferrucci, L., Goldberg, I.G.: Early detection of radiographic knee osteoarthritis using computer-aided analysis. Osteoarthritis Cartilage 17, 1307 (2009)

    Google Scholar 

  19. Shamir L., Ling S., Rahimi S., Ferruccic L., Goldberg I.G.: Biometric identification using knee X-rays. Int. J. Biometrics 1, 365–370 (2009)

    Article  Google Scholar 

  20. Shamir, L., Delaney, J.D., Orlov, N., Eckley, D.M., Goldberg, I.G.: Pattern recognition software and techniques for biological image analysis. PLOS Comput. Biol. 6 (2010)

  21. Gurevich I.B., Koryabkina I.V.: Comparative analysis and classification of features for image models. Pattern Recognit. Image Anal. 16, 265–297 (2006)

    Article  Google Scholar 

  22. Orlov N., Shamir L., Macura T., Johnston J., Eckley D.M., Goldberg I.G.: WND-CHARM: multi-purpose image classification using compound image transforms. Pattern Recognit. Lett. 29, 1684–1693 (2008)

    Article  Google Scholar 

  23. Leung T., Malik J.: Representing and recognizing the visual appearance of materials using three-dimensional textons. Int. J. Comput. Vis. 43, 29–44 (2001)

    Article  MATH  Google Scholar 

  24. Crosier M., Griffin L.D.: Using basic image features for texture classification. Int. J. Comput. Vis. 88, 447–460 (2010)

    Article  MathSciNet  Google Scholar 

  25. Ojala T., Maenpaa T., Pietikainen M.: Multiresolution grayscale and toration invariant texture classification with local library patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24, 971–987 (2002)

    Article  Google Scholar 

  26. Varma M., Zisserman A.: A statistical approach to material classification using image patch exemplars. IEEE Trans. Pattern Anal. Mach. Intell. 31, 2032–2047 (2009)

    Article  Google Scholar 

  27. Rodenacker K., Bengtsson E.: A feature set for cytometry on digitized microscopic images. Anal. Cell. Pathol. 25, 1–36 (2003)

    Google Scholar 

  28. Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: spatioal pyramid matching for recognizing natural scene categories. In: CVPR, NY, pp. 2169–2178 (2006)

  29. Fukunaga K.: Introduction to Statistical Pattern Recognition, 2nd edn. Academic Press, San Diego (1990)

    MATH  Google Scholar 

  30. Vapnik V.N.: Statistical Learninig Theory. Wiley-Interscience, New York (1998)

    Google Scholar 

  31. Duda R., Hart P., Stork D.: Pattern Classification, 2nd edn. Wiley, New York (2001)

    MATH  Google Scholar 

  32. Peng H., Long F., Ding C.: Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell. 27, 1226–1238 (2005)

    Article  Google Scholar 

  33. Samaria, F., Harter, A.: Parameterization of a stochastic model for human face identification. In: Second IEEE Workshop on Applications of Computer Vision, Saracota, FL, pp. 138–142 (1994)

  34. Belhumeur P.N., Hespanha J.P., Kriegman K.J.: Eigenfaces vs. Fisher-face: recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. 19, 711–720 (1997)

    Article  Google Scholar 

  35. Georghiades A.S., Belhumeur P.N., Kriegman D.J.: From few to many: illumination cone models for face recognition under variable lighting and pose. IEEE Trans. Pattern Anal. Mach. Intell. 23, 643–660 (2001)

    Article  Google Scholar 

  36. Duller A.W.G., Duller G.A.T., France I., Lanb H.F.: A pollen image database for evaluation of automated identification systems. Quat. Newslett. 89, 4–9 (1999)

    Google Scholar 

  37. Boland M., Markey M., Murphy R.: Automated recognition of patterns characteristic of subsellular structures in florescence microscopy images. Cytometry 33, 366–375 (1998)

    Article  Google Scholar 

  38. Shamir L., Macura T., Orlov N., Eckley D.M., Goldberg I.G.: ICBU 2008—a proposed benchmark suite for biological image analysis. Med. Biol. Eng. Comput. 46, 943–947 (2008)

    Article  Google Scholar 

  39. Brodatz P.: Textures. Dover, New York (1966)

    Google Scholar 

  40. Chebira, A., Barbotin, Y., Jackson, C., Merryman, T., Srinvasa, G., Murphy, R.F., Kovacevic, J.: A multiresolution approach to automated classification of protein subcellular location images. BMC Bioinformatics 8, 210 (2007)

    Google Scholar 

  41. Hu M.-K.: Visual pattern recognition by moment invariants. IRE Trans. Inf. Theory 8, 179–187 (1962)

    MATH  Google Scholar 

  42. Pavlidis T.: Algorithms for Graphics and Image Processing. Springer, Berlin (1982)

    Book  Google Scholar 

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Authors and Affiliations

  1. National Institute on Aging/National Institutes of Health, 251 Bayview Blvd., Bayview Research Center Bld., Suite 100, Baltimore, MD, 21224, USA

    Nikita V. Orlov, D. Mark Eckley, Lior Shamir & Ilya G. Goldberg

  2. Department of Mathematics and Computer Science, Lawrence Technological University, 21000 West Ten Mile Road, Southfield, MI, 48075-1058, USA

    Lior Shamir

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  1. Nikita V. Orlov
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  2. D. Mark Eckley
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  3. Lior Shamir
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  4. Ilya G. Goldberg
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Correspondence to Nikita V. Orlov.

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Orlov, N.V., Eckley, D.M., Shamir, L. et al. Improving class separability using extended pixel planes: a comparative study. Machine Vision and Applications 23, 1047–1058 (2012). https://doi.org/10.1007/s00138-011-0349-5

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  • DOI: https://doi.org/10.1007/s00138-011-0349-5

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