Automatic classification for solitary pulmonary nodule in CT image by fractal analysis based on fractional Brownian motion model

• We propose a fractal-based feature set for solitary pulmonary nodule classification.
• The proposed fractal-based feature set is derived from the fractional Brownian motion model.
• The classification results are evaluated by accuracy, sensitivity, specificity, PPV, NPV, and the area under ROC.
• Very high classification performance can be achieved by our proposed method.
• Distinction between malignant and benign nodule can be done in one single post-contrast CT scan.

Perfusion computed tomography (CT) method has been used to differentiate malignant pulmonary nodules from benign nodules based on the assessment for the change of the CT attenuation value within the pulmonary nodules. Instead of using the change of the CT attenuation value, a set of fractal features based on fractional Brownian motion model is proposed in this paper to automatically distinguish malignant nodules from benign nodules. In a set of 107 CT images from 107 different patients with each image containing a solitary pulmonary nodule, our experimental results obtained from a support vector machine classifier show that the accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and the area under the ROC curve are 83.11%, 90.92%, 71.70%, 80.05%, 87.52%, and 0.8437, respectively, by using the proposed fractal-based feature set. Such a result outperforms the conventional method of using the change of the CT attenuation value as the feature for classification. When combining this conventional method with our proposed fractal-based method, the accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and the area under the ROC curve can be promoted to 88.82%, 93.92%, 82.90%, 87.30%, 90.20%, and 0.9019, respectively. In other words, a high performance of pulmonary nodule classification can be achieved with a single post-contrast CT scan.