A Review on Lung Nodule Segmentation Techniques for Nodule Detection

摘要

In the medical domain of lung cancer diagnosis, automatic detection of lung nodules depends on the segmentation of different components related to pulmonary like airways, lobes, vessels from different types of imaging techniques such as CTs, MRIs, US, X-ray, etc., Since the biomedical image features are varying, segmentation of lung nodules in Computer Tomography (CT) images of the lung has always been a challenging task. Several types of algorithms viz. thresholding, regionbased, clustering, morphology, atlas, knowledge-based and edge-based are applied for the images to perform segmentation. The segmentation of lung nodules will help for the disease diagnosis with the changes associated in the lung. Present techniques are mainly focused on developing image analysis and preprocessing. This paper gives in depth survey on nodule segmentation accuracies in terms of a false positive, false negative, detection ratio, and error.