Advertisement

Biomedical Imaging Informatics for Diagnostic Imaging Marker Selection

  • Sonal Kothari Phan
  • Ryan Hoffman
  • May D. Wang
Chapter
Part of the Health Information Science book series (HIS)

Abstract

With the advent of digital imaging, thousands of medical images are captured and stored for future reference. In addition to recording medical history of a patient, these images are a rich source of information about disease-related markers. To extract robust and informative imaging markers, we need to regulate image quality, extract image features, select useful features, and validate them. Research and development of these computational methods fall under the science of biomedical imaging informatics. In this chapter, we discuss challenges and techniques of biomedical imaging informatics in the context of imaging marker extraction.

References

  1. 1.
    M.Y. Gabril, G.M. Yousef, Informatics for practicing anatomical pathologists: marking a new era in pathology practice. Mod. Pathol. 23, 349–358 (2010)CrossRefGoogle Scholar
  2. 2.
    S. Kothari, J.H. Phan, T.H. Stokes, M.D. Wang, Pathology imaging informatics for quantitative analysis of whole-slide images. J. Am. Med. Inform. Assoc. 20, 1099–1108 (2013)CrossRefGoogle Scholar
  3. 3.
    J.R. Swedlow, I.G. Goldberg, K.W. Eliceiri, Bioimage informatics for experimental biology. Ann. Rev. Biophys. 38, 327–346 (2009)CrossRefGoogle Scholar
  4. 4.
    H. Peng, Bioimage informatics: a new area of engineering biology. Bioinformatics 24, 1827–1836 (2008)CrossRefGoogle Scholar
  5. 5.
    K.W. Eliceiri, M.R. Berthold, I.G. Goldberg, L. Ibáñez, B.S. Manjunath, M.E. Martone et al., Biological imaging software tools. Nat. Methods 9, 697–710 (2012)CrossRefGoogle Scholar
  6. 6.
    Z. Xiaobo, S.T.C. Wong, Informatics challenges of high-throughput microscopy. IEEE Signal Process. Mag. 23, 63–72 (2006)CrossRefGoogle Scholar
  7. 7.
    L.R. Long, S. Antani, T.M. Deserno, G.R. Thoma, Content-based image retrieval in medicine: retrospective assessment, state of the art, and future directions. Int. j healthc. inform. syst. inform. official publ. Inf. Res. Manag. Assoc. 4, 1–16 (2009)CrossRefGoogle Scholar
  8. 8.
    U. Sinha, A. Bui, R. Taira, J. Dionisio, C. Morioka, D. Johnson et al., A review of medical imaging informatics. Ann. N. Y. Acad. Sci. 980, 168–197 (2002)CrossRefGoogle Scholar
  9. 9.
    T. Liu, H. Peng, X. Zhou, Imaging informatics for personalised medicine: applications and challenges. Int. J. Funct. Inf. Personalised med. 2, 125–135 (2009)CrossRefGoogle Scholar
  10. 10.
    A. Wetzel, computational aspects of pathology image classification and retrieval. J. Supercomputing 11, 279–293 (1997)CrossRefGoogle Scholar
  11. 11.
    T.J. Fuchs, J.M. Buhmann, Computational pathology: challenges and promises for tissue analysis. Comput. Med. Imaging Graph. 35, 515–530 (2011)CrossRefGoogle Scholar
  12. 12.
    W. Amin, U. Chandran, V. Parwani Anil, J. Becich Michael, in Essentials of Anatomic Pathology, ed. by L. Cheng, D. G. Bostwick. Biomedical Informatics for Anatomic Pathology, (Springer, New York, 2011), pp. 469–480Google Scholar
  13. 13.
    M.N. Gurcan, L. Boucheron, A. Can, A. Madabhushi, N. Rajpoot, B. Yener, Histopathological image analysis: a review. IEEE Rev. Biomed. Eng. 2, 147–171 (2009)CrossRefGoogle Scholar
  14. 14.
    E.T. Sadimin, D.J. Foran, Pathology imaging informatics for clinical practice and investigative and translational research. North Am. J. Med. Sci. (Boston) 5, 103–109 (2012)CrossRefGoogle Scholar
  15. 15.
    L. Pantanowitz, P.N. Valenstein, A.J. Evans, K.J. Kaplan, J.D. Pfeifer, D.C. Wilbur et al., Review of the current state of whole slide imaging in pathology. J. Pathol. Inform. 2, 36 (2011)CrossRefGoogle Scholar
  16. 16.
    R. McLendon, A. Friedman, D. Bigner, E.G. Van Meir, D.J. Brat, G.M. Mastrogianakis et al., Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 455, 1061–1068 (2008)CrossRefGoogle Scholar
  17. 17.
    S. Kothari, J. Phan, T. Stokes, A. Osunkoya, A. Young, M. Wang, Removing batch effects from histopathological images for enhanced cancer diagnosis. IEEE J. Biomed. Health Inform. 18, 765–772 (2014)CrossRefGoogle Scholar
  18. 18.
    S. Kothari, J. Phan, M. Wang, Eliminating tissue-fold artifacts in histopathological whole-slide images for improved image-based prediction of cancer grade. J. Pathol. Inf. 4, 22 (2013)CrossRefGoogle Scholar
  19. 19.
    Y. Saeys, I. Inza, P. Larrañaga, A review of feature selection techniques in bioinformatics. Bioinformatics 23, 2507–2517 (2007)CrossRefGoogle Scholar
  20. 20.
    S. Palokangas, J. Selinummi, O. Yli-Harja, in 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society Segmentation of folds in tissue section images, (2007), pp. 5642–5645Google Scholar
  21. 21.
    P. A. Bautista, Y. Yagi, in Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Detection of tissue folds in whole slide images, (2009), pp. 3669–3672Google Scholar
  22. 22.
    F.W. Leong, M. Brady, J.O.D. McGee, Correction of uneven illumination (vignetting) in digital microscopy images. J. Clin. Pathol. 56, 619–621 (2003)CrossRefGoogle Scholar
  23. 23.
    S. Kothari, J. H. Phan, R. A. Moffitt, T. H. Stokes, S. E. Hassberger, Q. Chaudry, et al., in IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Automatic batch-invariant color segmentation of histological cancer images, (2011), pp. 657–660Google Scholar
  24. 24.
    M. Macenko, M. Niethammer, J. S. Marron, D. Borland, J. T. Woosley, G. Xiaojun, et al., in 6th IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro, A method for normalizing histology slides for quantitative analysis,(2009), pp. 1107–1110Google Scholar
  25. 25.
    D. Magee, D. Treanor, D. Crellin, M. Shires, K. Smith, K. Mohee, et al., in Proc Optical Tissue Image analysis in Microscopy, Histopathology and Endoscopy (MICCAI Workshop), Colour Normalisation in Digital Histopathology Images, (2009), pp. 100–111Google Scholar
  26. 26.
    K. Jun, H. Shimada, K. Boyer, J. Saltz, M. Gurcan, in 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Image analysis for automated assessment of grade of neuroblastic differentiation, (2007), pp. 61–64Google Scholar
  27. 27.
    Q. Chaudry, S. Raza, A. Young, M. Wang, Automated renal cell carcinoma subtype classification using morphological, textural and wavelets based features. J. Sig Proc. Syst. 55, 15–23 (2009)CrossRefGoogle Scholar
  28. 28.
    C. Meurie, G. Lebrun, O. Lezoray, A. Elmoataz, A comparison of supervised pixels-based color image segmentation methods. application in cancerology. WSEAS Trans. Comput. 2, 44–739 (2003)Google Scholar
  29. 29.
    K. Mao, P. Zhao, P. Tan, Supervised learning-based cell image segmentation for P53 immunohistochemistry. IEEE Trans. Biomed. Eng. 53, 1153–1163 (2006)CrossRefGoogle Scholar
  30. 30.
    P. Ranefalla, L. Egevadb, B. Nordina, E. Bengtssona, A new method for segmentation of colour images applied to immunohistochemically stained cell nuclei. Anal. Cell. Pathol. 15, 145–156 (1997)CrossRefGoogle Scholar
  31. 31.
    Y. Al-Kofahi, W. Lassoued, W. Lee, B. Roysam, Improved automatic detection and segmentation of cell nuclei in histopathology images. IEEE Trans. Biomed. Eng. 57, 841–852 (2010)CrossRefGoogle Scholar
  32. 32.
    O. Sertel, J. Kong, U. Catalyurek, G. Lozanski, J. Saltz, M. Gurcan, Histopathological image analysis using model-based intermediate representations and color texture: follicular lymphoma grading. J. Sig. Process. Syst. 55, 169–183 (2009)CrossRefGoogle Scholar
  33. 33.
    C. Gunduz-Demir, M. Kandemir, A. Tosun, C. Sokmensuer, Automatic segmentation of colon glands using object-graphs. Med. Image Anal. 14, 1–12 (2010)CrossRefGoogle Scholar
  34. 34.
    J.P. Monaco, J.E. Tomaszewski, M.D. Feldman, I. Hagemann, M. Moradi, P. Mousavi et al., High-throughput detection of prostate cancer in histological sections using probabilistic pairwise Markov models. Med. Image Anal. 14, 617–629 (2010)CrossRefGoogle Scholar
  35. 35.
    P. Thevenaz, M. Unser, Snakuscules. IEEE Trans. Image Process. 17, 585–593 (2008)MathSciNetCrossRefGoogle Scholar
  36. 36.
    H. Kong, M. Gurcan, K. Belkacem-Boussaid, Partitioning histopathological images: an integrated framework for supervised color-texture segmentation and cell splitting. IEEE Trans. Med. Imaging 30, 1661–1677 (2011)CrossRefGoogle Scholar
  37. 37.
    A. Tabesh, M. Teverovskiy, P. Ho-Yuen, V.P. Kumar, D. Verbel, A. Kotsianti et al., Multifeature prostate cancer diagnosis and gleason grading of histological images. IEEE Trans. Med. Imaging 26, 1366–1378 (2007)CrossRefGoogle Scholar
  38. 38.
    T. Fuchs, P. Wild, H. Moch, J. Buhmann, in Medical Image Computing and Computer-Assisted Intervention, Computational Pathology Analysis of Tissue Microarrays Predicts Survival of Renal Clear Cell Carcinoma Patients, (2008), pp. 1–8Google Scholar
  39. 39.
    M. Rahman, P. Bhattacharya, B.C. Desai, A framework for medical image retrieval using machine learning and statistical similarity matching techniques with relevance feedback. IEEE Trans. Inf Technol. Biomed. 11, 58–69 (2007)CrossRefGoogle Scholar
  40. 40.
    L. Yang, O. Tuzel, W. Chen, P. Meer, G. Salaru, L.A. Goodell et al., PathMiner: a web-based tool for computer-assisted diagnostics in pathology. IEEE Trans. Inf Technol. Biomed. 13, 291–299 (2009)CrossRefGoogle Scholar
  41. 41.
    V. Kovalev, A. Dmitruk, I. Safonau, M. Frydman, and S. Shelkovich, in Computer Analysis of Images and Patterns, A Method for Identification and Visualization of Histological Image Structures Relevant to the Cancer Patient Conditions, vol. 6854, ed. by P. Real, D. Diaz-Pernil, H. Molina-Abril, A. Berciano, W. Kropatsch (Springer Berlin/Heidelberg, 2011), pp. 460–468Google Scholar
  42. 42.
    J. Kong, O. Sertel, H. Shimada, K.L. Boyer, J.H. Saltz, M.N. Gurcan, Computer-aided evaluation of neuroblastoma on whole-slide histology images: classifying grade of neuroblastic differentiation. Pattern Recogn. 42, 1080–1092 (2009)CrossRefGoogle Scholar
  43. 43.
    M.E. Celebi, H.A. Kingravi, B. Uddin, H. Iyatomi, Y.A. Aslandogan, W.V. Stoecker et al., A methodological approach to the classification of dermoscopy images. Comput. Med. Imaging Graph. 31, 362–373 (2007)CrossRefGoogle Scholar
  44. 44.
    M. Muthu Rama Krishnan, M. Pal, R. R. Paul, C. Chakraborty, J. Chatterjee, and A. K. Ray, in Journal of Medical Systems, Computer Vision Approach to Morphometric Feature Analysis of Basal Cell Nuclei for Evaluating Malignant Potentiality of Oral Submucous Fibrosis, vol. 36 (2012), pp. 1746–1756Google Scholar
  45. 45.
    L.A.D. Cooper, K. Jun, D.A. Gutman, W. Fusheng, S.R. Cholleti, T.C. Pan et al., An integrative approach for in silico glioma research. IEEE Trans. Biomed. Eng. 57, 2617–2621 (2010)CrossRefGoogle Scholar
  46. 46.
    S. Doyle, M. Feldman, J. Tomaszewski, A. Madabhushi, A boosted bayesian multi-resolution classifier for prostate cancer detection from digitized needle biopsies. IEEE Trans. Biomed. Eng. 59, 1205–1218 (2010)CrossRefGoogle Scholar
  47. 47.
    P.W. Huang, C.H. Lee, Automatic classification for pathological prostate images based on fractal analysis. IEEE Trans. Med. Imaging 28, 1037–1050 (2009)CrossRefGoogle Scholar
  48. 48.
    K. Jafari-Khouzani, H. Soltanian-Zadeh, Multiwavelet grading of pathological images of prostate. IEEE Trans. Biomed. Eng. 50, 697–704 (2003)CrossRefGoogle Scholar
  49. 49.
    D. Zhang, G. Lu, Review of shape representation and description techniques. Pattern Recogn. 37, 1 (2004)CrossRefGoogle Scholar
  50. 50.
    L. Boucheron, Object-and spatial-level quantitative analysis of multispectral histopathology images for detection and characterization of cancer, Ph.D thesis, University of California, Santa Barbara, 2008Google Scholar
  51. 51.
    C. Gunduz, B. Yener, H.S. Gultekin, The cell graphs of cancer. Bioinformatics 20, i145–i151 (2004)CrossRefGoogle Scholar
  52. 52.
    C.C. Bilgin, P. Bullough, G.E. Plopper, B. Yener, ECM-aware cell-graph mining for bone tissue modeling and classification. Data Min. Knowl. Disc. 20, 416–438 (2009)MathSciNetCrossRefGoogle Scholar
  53. 53.
    A.N. Basavanhally, S. Ganesan, S. Agner, J.P. Monaco, M.D. Feldman, J.E. Tomaszewski et al., Computerized image-based detection and grading of lymphocytic infiltration in HER2 + breast cancer histopathology. IEEE Trans. Biomed. Eng. 57, 642–653 (2010)CrossRefGoogle Scholar
  54. 54.
    J. Sudbø, R. Marcelpoil, A. Reith, New algorithms based on the Voronoi Diagram applied in a pilot study on normal mucosa and carcinomas. Anal. Cell. Pathol. 21, 71–86 (2000)CrossRefGoogle Scholar
  55. 55.
    J. Sudbo, A. Bankfalvi, M. Bryne, R. Marcelpoil, M. Boysen, J. Piffko et al., Prognostic value of graph theory-based tissue architecture analysis in carcinomas of the tongue. Lab. Invest. 80, 1881–1889 (2000)CrossRefGoogle Scholar
  56. 56.
    I. Guyon, A. Elisseeff, An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)zbMATHGoogle Scholar
  57. 57.
    M. A. Hall, “Correlation-based feature selection for machine learning,” Department of Computer Science, Waikato University, New Zealand, 1999Google Scholar
  58. 58.
    C. Ding, H. Peng, Minimum redundancy feature selection from microarray gene expression data. J. Bioinf. Comput. Biol. 3, 185–205 (2005)CrossRefGoogle Scholar
  59. 59.
    I. Kononenko, in Machine Learning: ECML-94, ed. by F. Bergadano, L. De Raedt. Estimating attributes: Analysis and extensions of RELIEF, vol. 784 (Springer Berlin/Heidelberg, 1994), pp. 171–182Google Scholar
  60. 60.
    D. B. Skalak, in Conference Processing on Machine Learning, Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms, (1994), pp. 293–301Google Scholar
  61. 61.
    J.H. Holland, Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence (Michigan University, Ann Arbor, 1975)zbMATHGoogle Scholar
  62. 62.
    S. Kirkpatrick, C.D. Gelatt, M.P. Vecchi, Optimization by simulated annealing. Science 220, 671 (1983)MathSciNetCrossRefzbMATHGoogle Scholar
  63. 63.
    I. Guyon, J. Weston, S. Barnhill, V. Vapnik, Gene selection for cancer classification using support vector machines. Mach. Learn. 46, 389–422 (2002)CrossRefzbMATHGoogle Scholar
  64. 64.
    S. B. Kotsiantis, in Informatica (03505596), Supervised machine learning: a review of classification techniques, vol. 31 (2007)Google Scholar
  65. 65.
    R. Bellazzi, B. Zupan, Predictive data mining in clinical medicine: current issues and guidelines. Int. J. Med. Inform. 77, 81–97 (2008)CrossRefGoogle Scholar
  66. 66.
    R. Hoffman, S. Kothari, J. Phan, M. D. Wang, in The International Conference on Health Informatics, ed. by Y.T. Zhang. A High-Resolution Tile-Based Approach for Classifying Biological Regions in Whole-Slide Histopathological Images, (Springer International Publishing, 2014), pp. 280–283Google Scholar
  67. 67.
    C.-C. Chang, C.-J. Lin, in ACM Transactions on Intelligent Systems and Technology (TIST), LIBSVM: a library for support vector machines, vol. 2 (2011), p. 27Google Scholar
  68. 68.
    S. Kothari, J. H. Phan, A. O. Osunkoya, M. D. Wang, in Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine, Biological interpretation of morphological patterns in histopathological whole-slide images, (2012), pp. 218–225Google Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Sonal Kothari Phan
    • 1
  • Ryan Hoffman
    • 1
  • May D. Wang
    • 1
    • 2
  1. 1.The Wallace H. Coulter Department of Biomedical EngineeringGeorgia Institute of Technology and Emory UniversityAtlantaUSA
  2. 2.School of Electrical and Computer EngineeringWinship Cancer Institute, Parker H. Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology and Emory UniversityAtlantaUSA

Personalised recommendations