Mohd Noor, Norliza, Mohd Rijal, Omar, Yunus, Ashari, Mahayiddin, Aziah Ahmad, Gan, Chew Peng, Ong, Ee Ling, and Abu Bakar, Syed Abdul Rahman
This chapter proposes a novel texture-based statistical procedure to detect and discriminate lobar pneumonia, pulmonary tuberculosis (PTB), and lung cancer simultaneously using digitized chest radiographs. A modified principal component method applied to wavelet texture measures yielded feature vectors for the statistical discrimination procedure. The procedure initially discriminated between a particular disease and the normals. The maximum column sum energy texture measure yielded 98 % correct classification rates for all three diseases. The diseases were then compared pair-wise, and the combination of mean of energy and maximum value texture measures gave correct classification rates of 70, 97, and 79 % for pneumonia, PTB, and lung cancer, respectively. [ABSTRACT FROM AUTHOR]