A novel Gaussian Scale Space-based joint MGRF framework for precise lung segmentation
A new framework for the precise segmentation of lung tissues from Computed Tomography (CT) is proposed. The CT images, Gaussian Scale Space (GSS) data generation using Gaussian Kernels (GKs), and desired maps of regions (lung and the other chest tissues) are described by a joint Markov-Gibbs Random Field Model (MGRF) of independent image signals and interdependent region labels. We focus on the most accurate model identification of the joint MGRF models. To better specify region borders, each empirical distribution of signals is rigorously approximated by a Linear Combination of Discrete Gaussians (LCDG) with positive and negative components. The classical Expectation-Maximization (EM) algorithm has been adapted for the LCDG model. The initial segmentations from the original and the generated GSS CT images are based on the LCDG-models; then they are iteratively refined using an MGRF model with analytically estimated potentials. Finally, these initial segmentations are fused together using a Bayesian fusion approach to get the final segmentation of the lung region. Experiments on eleven real data sets based on Dice Similarity Coefficient (DSC) metric confirms the high accuracy of the proposed approach.