A classification system of lung nodules in CT images based on fractional Brownian motion model

Abstract

In this paper, we present a classification system for differentiating malignant pulmonary nodules from benign nodules in computed tomography (CT) images based on a set of fractal features derived from the fractional Brownian motion (fBm) model. In a set of 107 CT images obtained from 107 different patients with each image containing a solitary pulmonary nodule, our experimental result show that the accuracy rate of classification and the area under the Receiver Operating Characteristic (ROC) curve are 83.11% and 0.8437, respectively, by using the proposed fractal-based feature set and a support vector machine classifier. Such a result demonstrates that our classification system has highly satisfactory diagnostic performance by analyzing the fractal features of lung nodules in CT images taken from a single post-contrast CT scan.