Probabilistic pairwise Markov models: application to prostate cancer detection

James Monaco; John E. Tomaszewski; Michael D. Feldman; Mehdi Moradi; Parvin Mousavi; Alexander Boag; Chris Davidson; Purang Abolmaesumi; Anant Madabhushi

doi:10.1117/12.812462

27 March 2009 Probabilistic pairwise Markov models: application to prostate cancer detection

James Monaco, John E. Tomaszewski, Michael D. Feldman, Mehdi Moradi, Parvin Mousavi, Alexander Boag, Chris Davidson, Purang Abolmaesumi, Anant Madabhushi

Author Affiliations +

Proceedings Volume 7259, Medical Imaging 2009: Image Processing; 725903 (2009) https://doi.org/10.1117/12.812462
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

Markov Random Fields (MRFs) provide a tractable means for incorporating contextual information into a Bayesian framework. This contextual information is modeled using multiple local conditional probability density functions (LCPDFs) which the MRF framework implicitly combines into a single joint probability density function (JPDF) that describes the entire system. However, only LCPDFs of certain functional forms are consistent, meaning they reconstitute a valid JPDF. These forms are specified by the Gibbs-Markov equivalence theorem which indicates that the JPDF, and hence the LCPDFs, should be representable as a product of potential functions (i.e. Gibbs distributions). Unfortunately, potential functions are mathematical abstractions that lack intuition; and consequently, constructing LCPDFs through their selection becomes an ad hoc procedure, usually resulting in generic and/or heuristic models. In this paper we demonstrate that under certain conditions the LCDPFs can be formulated in terms of quantities that are both meaningful and descriptive: probability distributions. Using probability distributions instead of potential functions enables us to construct consistent LCPDFs whose modeling capabilities are both more intuitive and expansive than typical MRF models. As an example, we compare the efficacy of our so-called probabilistic pairwise Markov models (PPMMs) to the prevalent Potts model by incorporating both into a novel computer aided diagnosis (CAD) system for detecting prostate cancer in whole-mount histological sections. Using the Potts model the CAD system is able to detection cancerous glands with a specificity of 0.82 and sensitivity of 0.71; its area under the receiver operator characteristic (AUC) curve is 0.83. If instead the PPMM model is employed the sensitivity (specificity is held fixed) and AUC increase to 0.77 and 0.87.

Citation Download Citation

James Monaco, John E. Tomaszewski, Michael D. Feldman, Mehdi Moradi, Parvin Mousavi, Alexander Boag, Chris Davidson, Purang Abolmaesumi, and Anant Madabhushi "Probabilistic pairwise Markov models: application to prostate cancer detection", Proc. SPIE 7259, Medical Imaging 2009: Image Processing, 725903 (27 March 2009); https://doi.org/10.1117/12.812462

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available