Lung Cancer Detection using CT SCAN Images

Abstract

Detection of pulmonary nodules has a crucial effect on the diagnosis of lung cancer, but the detection is a nontrivial task, not only because the appearance of pulmonary nodules varies in a wide range, but also because nodule densities have low contrast against adjacent vessel segments and other lung tissues. Computed tomography (CT) has been shown as the most popular imaging modality for nodule detection, because it has the ability to provide reliable image textures for the detection of small nodules. The development of lung nodule CADe systems using CT imaging modality has made good progress over the past decade. Generally, such CADe systems consist of three stages: 1) image pre-processing, 2) initial nodule candidates (INCs) identification, and 3) false positive (FP) reduction of the INCs with preservation of the true positives (TPs). In the pre-processing stage, the system aims to largely reduce the search space to the lungs, where a segmentation of the lungs from the entire chest volume is usually required. Because of the high image contrast between lung fields and the surrounding body tissue, image intensity-based simple thresholding is effective, and is currently the most commonly used technique for lung segmentation. This paper proposes an adaptive solution to mitigate the difficulty of thresholding-based method in lung segmentation. Sufficient detection power for nodule candidates is inevitably accompanied by many (obvious) FPs. A rule-based filtering operation is often employed to cheaply and drastically reduce the number of obvious FPs, so that their influence on the computationally more expensive learning process can be eliminated. In general, FP reduction using machine learning has been extensively studied in the literature. Compared with unsupervised learning that aims to find hidden structures in unlabelled data, supervised learning, which aims to infer a function from labelled training data, is more frequently used to design a CADe system. Compared with the existing approaches, the morphology based lung cancer detection can be an alternative with either comparable detection performance and less computational cost, or comparable cost and better detection performance.