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Knowledge Computing and its Applications pp 3–28Cite as

Probabilistic Graphical Models for Medical Image Mining Challenges of New Generation

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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.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Chaitanya Bharathi Institute of Technology (A), CBIT post, Gandipet, Hyderabad, Telangana, India

    Sridevi Tumula

  2. Department of Computer Science and Engineering, Osmania University, Hyderabad, Telangana, India

    Sameen S. Fathima

Authors
  1. Sridevi Tumula
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  2. Sameen S. Fathima
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Correspondence to Sridevi Tumula .

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Editors and Affiliations

  1. School of Computing Science and Engineering, VIT University, Vellore, Tamil Nadu, India

    S. Margret Anouncia

  2. The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense M, Denmark

    Uffe Kock Wiil

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Tumula, S., Fathima, S.S. (2018). Probabilistic Graphical Models for Medical Image Mining Challenges of New Generation. In: Margret Anouncia, S., Wiil, U. (eds) Knowledge Computing and its Applications. Springer, Singapore. https://doi.org/10.1007/978-981-10-8258-0_1

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  • DOI: https://doi.org/10.1007/978-981-10-8258-0_1

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  • Online ISBN: 978-981-10-8258-0

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