Authors : Anita Titus, H. Khanna Nehemiah and A. Kannan
Abstract: A Computer Aided Diagnosis (CAD) System for the detection of Interstitial Lung Diseases (ILDs) like emphysema, ground glass opacity, fibrosis and micro nodules based on the texture analysis of lung Computed Tomography (CT) slices has been proposed. The texture features are extracted from the lung region using the Gray Level Histogram (GLH). Quincunx Wavelet Transform (QWT) is applied to the lung regions and the distribution of the wavelet coefficients is modeled using the Gaussian Mixture Model (GSM) of two Gaussians with fixed mean and variable standard deviations. The standard deviations of the two Gaussians are estimated using the Expectation-Maximization (EM) algorithm. The feature vectors constructed from the texture features extracted using the GLH and the QWT are applied to the Support Vector Machine (SVM) classifier. The SVM classifier is optimized using particle swarm optimization and is used to classify the different lung tissue patterns. The classifier achieved an overall precision of 90.23%, accuracy of 96.01% and misclassification rate of 3.99%.
Anita Titus, H. Khanna Nehemiah and A. Kannan, 2015. Classification of Interstitial Lung Diseases Using Particle Swarm Optimized Support Vector Machine. International Journal of Soft Computing, 10: 25-36.