Malignancy Classification of Lung Nodule Based on Accumulated Multi Planar Views and Canonical Correlation Analysis

Appearance of a small round or oval shaped in a Computed Tomography (CT) scan of lung is an alarm to suspicion of lung cancer. In order to avoid the misdiagnose of lung cancer at early stage, Computer Aided Diagnosis (CAD) assists oncologists to classify pulmonary nodules as malignant (cancerous) or benign (noncancerous). This paper introduces a novel approach for pulmonary nodules classification employing three accumulated views (top, front, and side) of CT slices and Canonical Correlation Analysis (CCA). Nodule is extracted from 2D CT slice to obtain the Region of Interest (ROI) patch. All patches from sequential slices are accumulated from three different views. Vector representation of each view is correlated with two training sets, malignant and benign sets, employing CCA in spatial and Radon Transform (RT) domain. According to the correlation coefficients, each view is classified and the final classification decision is taken based on the priority decision. For training and testing, 1010 patients are downloaded from Lung Image Database Consortium (LIDC). The final results show that the proposed method achieved the best performance with an accuracy of 90.93% compared with existing methods.