Vol.114, No.2, 2018, pp.141-152, doi:10.3970/cmes.2018.114.141
OPEN ACCESS
ARTICLE
Automatic Delineation of Lung Parenchyma Based on Multilevel Thresholding and Gaussian Mixture Modelling
  • S. Gopalakrishnan1, *, A. Kandaswamy2
Department of Biomedical Engineering, PSG College of Technology, Coimbatore, Tamil Nadu, India. Email: sgk.bme@psgtech.ac.in
Department of Biomedical Engineering, PSG College of Technology, Coimbatore, Tamil Nadu, India. Email: akswamy.bme@psgtech.ac.in
* Corresponding author: S. Gopalakrishnan. Email: sgk.bme@psgtech.ac.in.
Abstract
Delineation of the lung parenchyma in the thoracic Computed Tomography (CT) is an important processing step for most of the pulmonary image analysis such as lung volume extraction, lung nodule detection and pulmonary vessel segmentation. An automatic method for accurate delineation of lung parenchyma in thoracic Computed Tomography images is presented in this paper. The proposed method involves a segmentation phase followed by a lung boundary correction technique. The tissues in the thoracic Computed Tomography can be represented by a number of Gaussians. We propose a histogram utilized Adaptive Multilevel Thresholding (AMT) for estimating the total number of Gaussians and their initial parameters. The parameters of Gaussian components are updated by Expectation Maximization (EM) algorithm. The segmented lung parenchyma from the Gaussian Mixture model (GMM) undergoes an Adaptive Morphological Filtering (AMF) to reduce the boundary errors. The proposed method has been tested on 70 diseased and 119 normal lung images from 28 cases obtained from Lung Image Database Consortium (LIDC). The performance of the proposed system has been validated.
Keywords
Lung parenchyma delineation, thoracic computed tomography, multilevel thresholding, Gaussian mixture model, Adaptive Morphological Filtering.
Cite This Article
Gopalakrishnan, S., Kandaswamy, A. (2018). Automatic Delineation of Lung Parenchyma Based on Multilevel Thresholding and Gaussian Mixture Modelling. CMES-Computer Modeling in Engineering & Sciences, 114(2), 141–152.