Texture-based classification for reticular pattern and ground glass opacity in high resolution computed tomography Thorax images

Lung disease is a global disease that affects a large group of people and is of growing interest to researches. The role of Computer Aided Diagnosis (CAD) systems to assist doctors to diagnose and detect disease is a beneficial one. Most techniques used to classify diseases stem from the textural method that is commonly associated with Gray Level Co-occurrence Matrix (GLCM). For this study, the objective is to classify the presence of two medical features in lung diseases which are Reticular Pattern (RP) and Ground Glass Opacity (GGO). A senior radiologist rates each slice and lung of a patient for the RP and GGO. Five slices of predetermined level of HRCT Thorax images that representing the whole lung of ten diseased patients and ten normal patients were used in this study. The textural features are extracted from each patient using the GLCM method. Classification was done using the WEKA, a machine learning tool. The classifiers used were the Naive Bayes, Multilayer Perceptron (MLP), K-Nearest Neighbor (KNN), Radial Basis Function Network, Random Forest and Decision Table classifiers. The classifiers showed that it is possible to have a classifier with an overall accuracy of 0.81 with RF classifier.