A morphological operation-based approach for Sub-pleural lung nodule detection from CT images

Abstract

Detection of pulmonary nodules is a frequent circumstance in daily radiology practice. Their extraction depends on their localization. Sub-pleural nodules are directly connected to the border of the pleura and have consequently open contours. Their extraction is challenging and a computer-aided diagnosis system is, hence, indispensable. In this paper, we propose an automatic segmentation approach of sub-pleural lung nodules from Computed Tomography (CT) scans based on morphological operations. This method is divided into three steps: pre-processing, initial detection of sub-pleural lung nodule and post-processing. First, a region of interest containing the nodule is extracted and converted using an adaptive thresholding algorithm. Second, morphological operations are used to create a mask for the lung lobe and segment sub-plural nodule. Finally, small structures connected to the border of the segmented image are removed and final nodule regions are detected. The proposed method is evaluated on 40 CT scan images (17 in axial acquisition and 23 in coronal reconstruction) and gives a good rate of accuracy that proves the effectiveness of our approach.