Study On Lung Nodule Detection Based On CT Image Analysis

Lung nodule is one of the most common pathological changes, thus early detection and diagnosis of lung nodule is very important for the medical treatment of lung cancer. In recent years, as the application of Multi-slice spiral CT, High-resolution CT and Low-dose chest CT, Computer-aided diagnosis (CAD) system will be more essential and more important. Thus a lot of methods emerge, which apply the image segmentation idea on detecting the pathological changes. Based on the realistic, the paper has done arithmetic research aiming to abstracting lung nodules from CT lung images.Lung nodule abstraction usually contains four steps:lung region segmentation, area-of-interest (ROI) abstraction, feature extraction and classification and recognition. Firstly, in lung region segmentation process, both interactive and non-interactive methods have been applied, which can be complimentary to each other. Both of them can accomplish the lung region image with robustness to a certain extent from different images. Secondly, in ROI abstraction, the paper uses three methods:fuzzy regional growing, mean shift with convergence index (CI) feature and fast marching method. Combining the mean shift and CI feature is a creation, which also acquires nice result. Thirdly, in the feature extraction, except the normal gray scale feature, shape feature and so on, the effect of fractal dimension in lung nodule detection is evaluated before adding it into the feature group. Finally, in classification and recognition, BP (Back Propagation) neural network is used.Besides the 2D abstraction, the most creative point of this paper is the successful 3D lung nodule abstraction by taking advantage of vessel reconstruction. Due to the circle shape of the lung vessel which is similar to lung nodules, the false positive rate is always quite high. The paper breaks the routine with the 3D reconstruction of vessels in lung as the beginning, then avoid the interference indirectly. First, we get the 2D complete lung regions. Second, the lung soft tissue is acquired by region growing method, so we can get the possible nodules. Third, use the geometrical meaning of 3D Hessian matrix's Eigen values to finish vessel 3D enhancement and reconstruction. Then we compare the vessels with the possible nodule image, remove the overcast regions. Finally, we remove the small lung regions with the geometry features. The paper gets low false positive and high accuracy.