Suspected pulmonary nodule detection algorithm based on morphology and gray entropy

Abstract

A suspected pulmonary nodule detection algorithm in CT images has been presented. This algorithm is mainly based on the multi-scale filtering of morphology and the selecting of gray entropy. First, lung parenchyma is segmented from original CT image effectively and accurately. According to the different geometry shapes of pulmonary nodules in CT image, three circle-like structure elements with different dimensions are built, and the multi-scale morphologic filtering is adopted to get the initial candidates of the regions of interest (ROI). After this processing, the circular regions which are bigger than the structure elements are enhanced while the linear regions including bronchus and blood-vessels are suppressed; secondly, in order to further reduce the number of ROI, according to the gray variation differences between pulmonary nodules and bronchus and blood-vessels, gray entropy is adopted to distinguish the pulmonary nodules from others. Experiment results indicate that the algorithm can extract suspected pulmonary nodule regions in the CT images effectively, which is a basis for subsequent pulmonary nodule identification and diagnose.