Fractal analysis for computer-aided diagnosis of diffuse pulmonary diseases in HRCT images

The purpose of this article is to propose the use of fractal and texture analysis for computer-aided diagnosis (CAD) of diffuse pulmonary diseases (DPDs) in high-resolution computed tomography (HRCT) images. We propose multiple techniques to extract features from preprocessed regions of interest (ROIs) selected to represent five radiographic patterns useful in the differential diagnosis of DPDs, as well as normal cases. First-order statistics of gray-level distribution, Haralick's and Laws' texture features, statistical information extracted from the ROIs' discrete Fourier transforms, and their fractal dimension values were used as attributes. The features were used as inputs for a k-nearest neighbor classifier (k=5). With a dataset of 3252 ROIs, correct classification rates of up to 82.62% were achieved.