CT Lung Images Segmentation Using Image Processing And Markov Random Field

Abstract

Introduction: In this study, the performance of computed tomography lung image segmentation using image processing and Markov Random Field was investigated. Before cancer segmentation and analysis, lung segmentation is an important initial process. Thus, the aim of this study is to find the optimal Markov Random Field setting for lung segmentation. Methods: The Centre for Diagnostic Nuclear Imaging at UPM provided 11 anonymous sets of cancerous lung CT images for this study. The thresholding technique is an effective method for medical image segmentation when the priori information for the region of interest is known, such as the Hounsfield Unit value of lung. Due to the large differences in grey levels in the image, the thresholding approach is difficult to apply in segmentation, especially for lung. Thus, for the segmentation process, this study used multilevel thresholding with Markov Random Field with three settings; Iterated Condition Mode, Metropolis algorithm, and Gibbs sampler. The images then went through image processing procedures which were binarization, small object removal, lung region extraction and lung segmentation. The output from the experiments were analyzed and compared to determine the ideal lung segmentation setting. Results: The Jaccard index average values; Markov Random Field -Metropolis = 0.9464, Markov Random Field -ICM = 0.9499 and Markov Random Field -Gibbs = 0.9512. The Dice index average values; Markov Random Field - Metropolis = 0.9743, Markov Random Field - ICM = 0.9724 and Markov Random Field - Gibbs = 0.9749. Conclusion: Markov Random Field using Gibbs sampler delivered the best results for lung segmentation.