An Automatic Computerized Model for Cancerous Lung Nodule Detection from Computed Tomography Images with Reduced False Positives

The objective of this work is to identify the malignant lung nodules accurately and early with less false positives. In our work block histogram based auto center seed k-means clustering technique is used to segment all the possible nodule candidates. Efficient shape and texture features (2D and 3D) were computed to eliminate the false nodule candidates. The two-stage classifier is used in this work to classify the malignant and benign nodules. First stage rule-based classifier producing 100% sensitivity, but with high false positive of 13.1 per patient scan. The BPN based ANN classifier is used as the second-stage classifier which reduces a false positive to 2.26 per patient scan with a good sensitivity of 88.8%. The nodule growth predictive measure was modeled through the features such as tissue deficit, tissue excess, isotropic factor and edge gradient. The overlap of these measures for larger, average and minimum nodule growth cases are less. Therefore this developed growth prediction model can be used to assist the physicians while taking the decision on the cancerous nature of lung nodules from an earlier CT scan.