Multilevel Thresholding Based Segmentation and Feature Extraction for Pulmonary Nodule Detection

The identification of pulmonary nodules in humans has always been a vexing problem in the field of medical electronics. In this paper, an approach is proposed for pulmonary nodule segmentation and feature extraction using multilevel thresholding. The suitably extracted features can go a long way in the efficient detection of pulmonary nodules, which in turn can improve the chances for successful classification of nodules. The proposed segmentation with three level thresholding along with the features extracted can be incorporated to any suitable classification architecture to detect pulmonary nodules with better accuracy.