Statistical modeling of the lung nodules in low dose computed tomography scans of the chest

Abstract

This work presents a novel approach in automatic detection of the lung nodules and is compared with respect to parametric nodule models in terms of sensitivity and specificity. A Statistical method is used for generating data driven models of the nodules appearing in low dose CT (LDCT) scans of the human chest. Four types of common lung nodules are analyzed using the Procrustes based AAM method to create descriptive lung nodules. Performance of the new nodule models on clinical datasets is significant over parametric nodule models in both sensitivity and specificity. The new nodule modeling approach is also applicable for automatic classification of nodules into pathologies given a descriptive database. This approach is a major step forward for early diagnosis of lung cancer.