A New Stochastic Framework for Accurate Lung Segmentation

Authors:
Ayman El-Ba;Georgy Gimel'Farb;Robert Falk;Trevor Holland;Teresa Shaffer
Affiliations:
Bioimaging Laboratory, Bioengineering Department, University of Louisville, Louisville, USA;Department of Computer Science, University of Auckland, Auckland, New Zealand;Director, Medical Imaging Division, Jewish Hospital, , Louisville, USA;Bioimaging Laboratory, Bioengineering Department, University of Louisville, Louisville, USA;Bioimaging Laboratory, Bioengineering Department, University of Louisville, Louisville, USA
Venue:
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Year:
2008

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Abstract

New techniques for more accurate unsupervised segmentation of lung tissues from Low Dose Computed Tomography (LDCT) are proposed. In this paper we describe LDCT images and desired maps of regions (lung and the other chest tissues) by a joint Markov-Gibbs random field model (MGRF) of independent image signals and interdependent region labels but focus on most accurate model identification. To better specify region borders, each empirical distribution of signals is precisely approximated by a Linear Combination of Discrete Gaussians (LCDG) with positive and negative components. We modify a conventional Expectation-Maximization (EM) algorithm to deal with the LCDG and develop a sequential EM-based technique to get an initial LCDG-approximation for the modified EM algorithm. The initial segmentation based on the LCDG-models is then iteratively refined using a MGRF model with analytically estimated potentials. Experiments on real data sets confirm high accuracy of the proposed approach.