Texture Measures for Automatic Classification of Pulmonary Disease

Abstract

The complete pattern recognition problem is considered for the practical solution to a current significant medical question. Automated screening of chest radiographs for the detection of textural type abnormalities is approached from the view of: 1) preprocessing for standardization and data reduction; 2) feature extraction of characteristic measures (feature selection by optimization of classification accuracy); and 3) overall classification using training and test sets of selected chest radiographs.