An Improved Automatic Lung Segmentation Algorithm for Thoracic CT Image Based on Features Selection

In order to evaluate thoracic disease, lung segmentation is a basic and necessary step. In computed tomography, different methods have been developed for automatic lung segmentation. Deep learning models are considered as one of the most important approaches in this field because it can process a lot of images in a timely manner. However, in order for deep learning work efficiently, it needs significant large dataset with precise manually lung segmentation. In this article, we concentrate on approaches and algorithms for image processing to work with pixels value of images. We are able to remove lung mask with high precision from initial computed tomography images by managing the pixel value array. By evaluating the pixel value of its binary image output with 2-D ground truth images, automatic segmentation is assessed. The result shows that for most sections of thoracic pictures, this procedure worked well and in case if there is any fading in the image, such as bone or soft tissue, the algorithm needed to be enhanced.