A Markov random field approach for CT image lung classification using image processing. (November 2022)

• Record Type: Journal Article
• Authors: Aziz, K.A.A.; Saripan, M.I.; Saad, F.F.A.; Abdullah, R.S.A.R.; Waeleh, N.
• Is Part Of: Radiation physics and chemistry. Volume 200(2022)
• Publication Date: 2022-11
• Subjects: Computed tomography -- Image processing -- Lung classification -- Markov random field -- Lung segmentation; Radiation chemistry -- Periodicals; Radiometry -- Periodicals; Radiation -- Periodicals; Chimie sous rayonnement -- Périodiques; 539.2
• DOI: 10.1016/j.radphyschem.2022.110440
• Languages: English
• ISSNs: 0969-806X

Abstract

Abstract: The performance of computed tomography lung classification using image processing and Markov Random Field was investigated in this study. For lung classification, the process must first be going through lung segmentation process. Lung segmentation is important as an initial process before lung cancer segmentation and analysis. Image processing was employed to the input image. We propose multilevel thresholding and Markov Random Field to improve the segmentation process. Three setting for Markov Random Field was used for segmentation process that is Iterated Condition Mode, Metropolis algorithm and Gibbs sampler. Then, the process of classifying lung will proceed. The output from the experiments were analysed and compared to get the best performance. The results revealed that for CT image lung classification, Markov Random Field using Metropolis algorithm gives the best results. In view of the result obtained, the average accuracy is 94.75% while the average sensitivity and specificity are 76.34% and 99.80%. The output from this study can be implemented in lung cancer analysis research and computer aided diagnosis development. Highlights: Lung segmentation is important as an initial process before lung cancer segmentation and analysis as well as classification. Multilevel thresholding and Markov Random Field can improve the classification results. Three different setting for Markov Random Field i. e Metropolis algorithm, Iterated Condition Mode and Gibbs sampler.