Probabilistic Graphical Models for Medical Image Mining Challenges of New Generation

Chapter

Abstract

Probabilistic graphical models (PGM) are one of the rich frameworks. These models are used over complex domains for coding probability distributions. The joint distributions interact with each other over large numbers of random variables and are the combination of statistics and computer science. These concepts are dependent on theories such as probability theory, graph algorithms, machine learning, which make a basic tool in devising many machine learning problems. These are the origin for the contemporary methods in an extensive range of applications. These applications range as medical diagnosis, image understanding, speech recognition, natural language processing, etc. Graphical models are one of dominant tools for handling image processing applications. On the other hand, the volume of image data gives rise to a problem. The representation of all possible graphical model node variables with that of discrete states heads to the number of states for the model. This leads to interpretation computationally obstinate. Many projects involve a human intervention or an automated system to obtain the consensus established on existing information. The PGM, discussed in this chapter, offers a variety of approaches. The approach is based on models and allows interpretable models to be built which then is employed by reasoning algorithms. These models are also studied significantly from data and allow the approaches for cases where the model is manually built. Most real-world applications are of uncertain data which makes a model building more challenging. This chapter emphasizes on PGM where the uncertainty of data is obvious. PGM provides models that are more realistic. These are extended from Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data also. For each class of models, the chapter describes the fundamental bases: representation, inference, and learning. Finally, the chapter considers the decision making under the uncertainty of the data.

Keywords

PGM Probability theory Graph algorithms Machine learning Bayesian networks Undirected Markov networks Discrete and continuous models 

References

  1. 1.
    Christopher, M. Bishop pattern recognition and machine learning.Google Scholar
  2. 2.
  3. 3.
    Moustakis, V., & Charissis, G. (1999). Machine learning and medical decision making. In Proceedings of Workshop on Machine Learning in Medical Applications, Advance Course in Artificial Intelligence ACAI99, Chania, Greece (pp. 1–19). Google Scholar
  4. 4.
    Fung, G., Krishnapuram, B., Bi, J., Dundar, M., Raykar, V., Yu, S., et al. (2009). Mining medical images. In: Third Workshop on Data Mining Case Studies and Practice Prize, 15th Annual SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, France.Google Scholar
  5. 5.
    Sudhir, R. (2011). A Survey on image mining techniques theory and applications. Computer Engineering and Intelligent Systems, 2(6).Google Scholar
  6. 6.
    Ordonez, C., & Omiecinski, E. (1999). Discovering association rules based on image content. In Proceedings of the IEEE Advances in Digital Libraries Conference (ADL’99).Google Scholar
  7. 7.
    Megalooikonomou, V., Davataikos, C., & Herskovits, E. (1999). Mining lesion-deficit associations in a brain image database. San Diego, CA, USA: KDD.Google Scholar
  8. 8.
    Missaoui, R., & Palenichka, R. M. (2005). Effective image and video mining: An overview of model based approaches. In MDM’05: Proceedings of the 6th International Workshop on Multimedia Data Mining (pp. 43–52).Google Scholar
  9. 9.
    Fernandez, J., Miranda, N., Guerrero, R., & Piccoli, F. (2007) Appling Parallelism in Image Mining. www.ing.unp.edu.ar/wicc2007/trabajos/PDP/120.pdf.
  10. 10.
    Sanjay, T., et al. (2007). Image mining using wavelet transform. In Knowledge-based intelligent information and engineering systems (pp. 797–803). Springer Link Book Chapter.Google Scholar
  11. 11.
    Pattnaik, S., Das Gupta, P. K., & Nayak, M. (2008). Mining images using clustering and data compressing techniques. International Journal of Information and Communication Technology, 1(2), 131–147.Google Scholar
  12. 12.
    Kralj, K., & Kuka, M. (1998). Using machine learning to analyze attributes in the diagnosis of coronary artery disease. In Proceedings of Intelligent Data Analysis in Medicine and Pharmacology-IDAMAP98, Brighton, UK.Google Scholar
  13. 13.
    Zupan, B., Halter, J. A., & Bohanec, M. (1998). Qualitative model approach to computer assisted reasoning in physiology. In Proceedings of Intelligent Data Analysis in Medicine and Pharmacology-IDAMAP98, Brighton, UK.Google Scholar
  14. 14.
    Bourlas, P., Sgouros, N., Papakonstantinou, G., & Tsanakas, P. (1996). Towards a knowledge acquisition and management system for ECG diagnosis. In Proceedings of 13th International Congress Medical Informatics Europe-MIE96, Copenhagen.Google Scholar
  15. 15.
    Bratko, I., Mozetič, I., & Lavrač, N. (1989). KARDIO: A study in deep and qualitative knowledge for expert systems. Cambridge, MA: MIT Press.Google Scholar
  16. 16.
    Hau, D., & Coiera, E. (1997). Learning qualitative models of dynamic systems. Machine Learning, 26, 177–211.CrossRefMATHGoogle Scholar
  17. 17.
    Akay, Y. M., Akay, M., Welkowitz, W., & Kostis, J. B. (1994). Noninvasive detection of coronary artery disease using wavelet-based fuzzy neural networks. IEEE Engineering in Medicine and Biology, 761–764.Google Scholar
  18. 18.
    Lim, C. P., Harrison, R. F., & Kennedy, R. L. (1997). Application of autonomous neural network systems to medical pattern classification tasks. Artificial Intelligence in Medicine, 11, 215–239.CrossRefGoogle Scholar
  19. 19.
    Lo, S.-C. B., Lou, S.-L. A., Lin, J.-S., Freedman, M. T., Chien, M. V., & Mun, S. K. (1995). Artificial convolution neural network techniques and applications for lung nodule detection. IEEE Transactions on Medical Imaging, 14, 711–718.CrossRefGoogle Scholar
  20. 20.
    Micheli-Tzanakou, E., Yi, C., Kostis, W. J., Shindler, D. M., & Kostis, J. B. (1993). Myocardial infarction: Diagnosis and vital status prediction using neural networks. IEEE Computers in Cardiology, 229–232.Google Scholar
  21. 21.
    Miller, A. S., Blott, B. H., & Hames, T. K. (1992). Review of neural network applications in medical imaging and signal processing. Medical & Biological Engineering & Computing, 30, 449–464.CrossRefGoogle Scholar
  22. 22.
    Nekovei, R., & Sun, Y. (1995). Back-propagation network and its configuration for blood vessel detection in angiograms. IEEE Transactions on Neural Networks, 6(1), 64–72.CrossRefGoogle Scholar
  23. 23.
    Neves, J., Alves, V., Nelas, L., Romeu, A., & Basto, S. (1999). An information system that supports knowledge discovery and data mining in medical imaging. In Proceedings of Workshop on Machine Learning in Medical Applications, Advance Course in Artificial Intelligence-ACAI99, Chania, Greece (pp. 37–42).Google Scholar
  24. 24.
    Pattichis, C., Schizas, C., & Middleton, L. (1995). Neural network models in EMG diagnosis. IEEE Transactions on Biomedical Engineering, 42(5), 486–496.CrossRefGoogle Scholar
  25. 25.
    Phee, S. J., Ng, W. S., Chen, I. M., Seow-Choen, F., & Davies, B. L. (1998). Automation of colonoscopy part II: Visual-control aspects. IEEE Engineering in Medicine and Biology, 81–88.Google Scholar
  26. 26.
    Pouloudi, A. (1999). Information technology for collaborative advantage in health care revisited. Information and Management, 35(6), 345–357; Rayburn et al. 1990.Google Scholar
  27. 27.
    Yeap, T. H., Johnson, F., & Rachniowski, M. (1990). ECG beat classification by a neural network. In Proceedings of the 12th Annual International Conference of the IEEE Engineering in Medicine and Biology Society Philadelphia, Pennsylvania, USA (Vol. 3, pp. 1457–1458).Google Scholar
  28. 28.
    Coppini, G., Poli, R., & Valli, G. (1995). Recovery of the 3-D shape of the left ventricle from echocardiographic images. IEEE Transactions on Medical Imaging, 14, 301–317.CrossRefGoogle Scholar
  29. 29.
    Hanka, R., Harte, T. P., Dixon, A. K., Lomas, D. J., & Britton, P. D. (1996). Neural networks in the interpretation of contrast-enhanced magnetic resonance images of the breast. In Proceedings of Healthcare Computing, Harrogate, UK (pp. 275–283).Google Scholar
  30. 30.
    Innocent, P. R., Barnes, M., & John, R. (1997). Application of the fuzzy ART/MAP and MinMax/MAP neural network models to radiographic image classification. Artificial Intelligence in Medicine, 11, 241–263.CrossRefGoogle Scholar
  31. 31.
    Karkanis, S., Magoulas, G. D., Grigoriadou, M., & Schurr, M. (1999). Detecting abnormalities in colonoscopic images by textural description and neural networks. In Proceedings of Workshop on Machine Learning in Medical Applications, Advance Course in Artificial Intelligence-ACAI99, Chania, Greece, pp. 59–62.Google Scholar
  32. 32.
    Veropoulos, K., Campbell, C., & Learmonth, G. (1998). Image processing and neural computing used in the diagnosis of tuberculosis. In Colloquium on intelligent methods in healthcare and medical applications. UK.Google Scholar
  33. 33.
    Zhu, Y., & Yan, H. (1997). Computerized tumor boundary detection using a Hopfield neural network. IEEE Transactions on Medical Imaging, 16, 55–67.CrossRefGoogle Scholar
  34. 34.
    Delaney, P. M., Papworth, G. D., & King, R. G. (1998). Fibre optic confocal imaging (FOCI) for in vivo subsurface microscopy of the colon. In V. R. Preedy & R. R. Watson (Eds.), Methods in disease: Investigating the gastrointestinal tract. London: Greenwich Medical Media.Google Scholar
  35. 35.
    Anderson, J. G. (1997). Clearing the way for physician’s use of clinical information systems. Communications of the ACM, 40(8), 83–90.CrossRefGoogle Scholar
  36. 36.
    Lane, V. P., Lane, D., & Littlejohns, P. (1996). Neural networks for decision making related to asthma diagnosis and other respiratory disorders. In Proceedings of Healthcare Computing, Harrogate, UK (pp. 85–93).Google Scholar
  37. 37.
    Ridderikhoff, J., & van Herk, B. (1999). Who is afraid of the system? Doctors’ attitude towards diagnostic systems. International Journal of Medical Informatics, 53, 91–100.CrossRefGoogle Scholar
  38. 38.
    Sudhir, R. (2011). A survey on image mining techniques theory and applications. Computer Engineering and Intelligent Systems, 2(6).Google Scholar
  39. 39.
    Pearl, J. (1988). Probabilistic reasoning in intelligent systems: Networks of plausible inference. Morgan Kaufmann.Google Scholar
  40. 40.
    Boykov, Y., Veksler, O., & Zabih, R. (2001). Fast approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(11), 1222–1239.CrossRefGoogle Scholar
  41. 41.
    Yousofi, M. H., Esmaeili, M., & Sharifian, M. S. (2016). A study on image mining; its importance and challenges. American Journal of Software Engineering and Applications, 5(3–1), 5–9.Google Scholar
  42. 42.
  43. 43.
    Machine Learning and Medical Imaging 1st Edition Academic Press. (2016). eBook ISBN: 9780128041147; Hardcover ISBN: 9780128040768.Google Scholar
  44. 44.
    Tappen, M. F., Russell, B. C., & Freeman, W. T. (2004). Efficient graphical models for processing images. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR).Google Scholar
  45. 45.
    Wainwright, M. J., & Jordan, M. I. (2008). Graphical models, exponential families, and variational inference. Foundations and Trends® in Machine Learning, 1(1–2), 1–305.  https://doi.org/10.1561/2200000001.

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringChaitanya Bharathi Institute of Technology (A)Gandipet, HyderabadIndia
  2. 2.Department of Computer Science and EngineeringOsmania UniversityHyderabadIndia

Personalised recommendations