A classification system of lung nodules in CT images based on fractional Brownian motion model (original) (raw)

2013 International Conference on System Science and Engineering (ICSSE), 2013

Abstract

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.

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