Fast fractal stack: fractal analysis of computed tomography scans of the lung

Abstract

This paper proposes a new feature extraction method: the Fast Fractal Stack, or FFS. The extraction algorithm consists in decomposing the input grayscale image into a stack of binary images from which the fractal dimension values are computed, resulting in a compact and highly descriptive set of features. We evaluated FFS for the task of classification of interstitial lung diseases in computed tomography (CT) scans, applied on a database of 248 CT images from 67 patients. The proposed approach performs well, improving the classification accuracy when compared to other feature extraction algorithms. Additionally, the FFS extraction algorithm is efficient, with a computational cost linear with respect to input image size.