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Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe): Distinguishing Tumor Confounders and Molecular Subtypes on MRI

  • Prateek Prasanna
  • Pallavi Tiwari
  • Anant Madabhushi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8675)

Abstract

We introduce a novel biologically inspired feature descriptor, Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe), that captures higher order co-occurrence patterns of local gradient tensors at a pixel level to distinguish disease phenotypes that have similar morphologic appearances. A number of pathologies (e.g. subtypes of breast cancer) have different histologic phenotypes but similar radiographic appearances. While texture features have been previously employed for distinguishing subtly different pathologies, they attempt to capture differences in global intensity patterns. In this paper we attempt to model CoLlAGe to identify higher order co-occurrence patterns of gradient tensors at a pixel level. The assumption behind this new feature is that different pathologies, even though they may have very similar overall texture and appearance on imaging, at a local scale, will have different co-occurring patterns with respect to gradient orientations. We demonstrate the utility of CoLlAGe in distinguishing two subtly different types of pathologies on MRI in the context of brain tumors and breast cancer. In the first problem, we look at CoLlAGe for distinguishing radiation effects from recurrent brain tumors over a cohort of 40 studies, and in the second, discriminating different molecular subtypes of breast cancer over a cohort of 73 studies. For both these challenging cohorts, CoLlAGe was found to have significantly improved classification performance, as compared to the traditional texture features such as Haralick, Gabor, local binary patterns, and histogram of gradients.

Keywords

Local Binary Pattern Molecular Subtype Texture Descriptor Triple Negative Pixel Level 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Agner, S., et al.: Computerized image analysis for identifying triple-negative breast cancers and differentiating them from other molecular subtypes of breast cancer on dce-mri. Radiology (pre-print, 2014)Google Scholar
  2. 2.
    Haralick, R.M., et al.: Textural Features for Image Classification. Systems, Man and Cybernetics (6), 610–621 (1973)Google Scholar
  3. 3.
    Ojala, T., et al.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. PAMI 24(7), 971–987 (2002)CrossRefGoogle Scholar
  4. 4.
    Dalal, N., Triggs, B.: Histograms of Oriented Gradients forHuman Detection. In: CVPR 2005, vol. 1, pp. 886–893. IEEE (2005)Google Scholar
  5. 5.
    Pang, Y., et al.: Robust Cohog Feature Extraction in Human-Centered Image/Video Management System. Systems, Man, and Cybernetics 42(2), 458–468 (2012)CrossRefGoogle Scholar
  6. 6.
    Bengio, Y., Courville, A.C., Vincent, P.: Unsupervised feature learning and deep learning: A review and new perspectives. CoRR (2012)Google Scholar
  7. 7.
    Tiwari, P., et al.: Texture descriptors to distinguish radiation necrosis from recurrent brain tumors on multi-parametric mri. In: SPIE, pp. 90352B–90352B (2014)Google Scholar
  8. 8.
    Furey, T.S., et al.: Svm Classification and Validation of Cancer Tissue Samples using Microarray Expression Data. Bioinformatics 16(10), 906–914 (2000)CrossRefGoogle Scholar
  9. 9.
    Wilcoxon, F., Wilcox, R.A.: Some Rapid Approximate Statistical Procedures. Lederle Laboratories (1964)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Prateek Prasanna
    • 1
  • Pallavi Tiwari
    • 1
  • Anant Madabhushi
    • 1
  1. 1.Department of Biomedical EngineeringCase Western Reserve UniversityUSA

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