LAUSR

ABSTRACT Computer-aided detection and diagnosis (CAD) of lung-related diseases will be helpful for early detection. Lung parenchyma segmentation is considered as a prerequisite for most of CAD systems. The available traditional methods for lung parenchyma segmentation are not accurate because the nodules that adhere to the lung pleura are recognized as fat. This paper proposes an automated lung parenchyma segmentation for accurate detection of lung nodules, mainly juxtapleural nodules. The proposed method includes the bidirectional chain code to improve the segmentation, and the support vector machine classifier is used to avoid false inclusion of regions. The proposed method is verified on various datasets for robustness of the algorithm. This automated method provides an accuracy of 97% in segmentation compared to ground truth results obtained by experts, which drastically reduces the complexity and intervention of a radiologist.