Segmentation of differential structures on computed tomography images for diagnosis lung-related diseases

Computer-aided diagnostics (CAD) systems for automatic detection of lung cancer or lung-related diseases have highly depended on the segmentation accuracy of differential structures from computed tomography (CT) scan images. By detection of differential structures such as right/left Lungs, lung nodules, human airways and pulmonary trees, the new segmentation algorithm (PropSeg) is proposed. The PropSeg method is developed based on four major phases such as pre-processing, detection of candidate regions, segmentation, and post-processing. The pre-processing step is performed to enhance by reconstruction of an input image into the 4 frequency subbands through discrete wavelet transform (DWF) and un-sharp energy mask (UEM). The 3 levels of fuzzy c-means (FCM) clustering is used to detect candidate regions by an integration of local energy constraints (LEC) and variational level set (VLS) method is then utilized to segment differential regions. Moreover, the post-processing step is performed by morphological edge detection to enhance the results of segmentation. The system is tested with manually draw radiologist contours on the 220 images by using statistical measures. The performance of PropSeg is also compared with other four state-of-the-art segmentation methods. The achieve results show that the PropSeg system is outperformed compared to other techniques and it is favorable for automatic diagnosis of lung cancer or to detect lung-related diseases. (C) 2016 Elsevier Ltd. All rights reserved.