Detection of pulmonary nodules using thresholding and fractal analysis

Abstract

Automated detection of pulmonary nodules helps radiologists in early detection of lung cancer from computed tomography (CT) scans. It is very costly computationally because of its complexity of the process. The CT scan has more advantages than other computational algorithms. The preprocessed CT scan is thresholded using Otsu’s method and the lung region is segmented using K-means Clustering which is based on geometric features. Texture based feature analysis algorithm is used to identify the major descriptors. The Artificial Neural Networks (ANN) is used for training, testing and validation process takes place to identify nodule and classify in stages i.e. Stage 1 (initial), Stage 2 (middle) and Stage 3 (critical). The results obtained in this method has been checked for accuracy.