Automatic lung segmentation in chest radiographs using shadow filter and local thresholding

Abstract

Lung segmentation is one of the essential steps in order to develop a Computer-aided Diagnosis (CAD) system for detection of some chest diseases in chest radiographs such as tuberculosis, lung cancer, atelectasis, etc. This paper proposes an unsupervised learning method for lung segmentation in chest radiographs based on shadow filter and local thresholding. The approach consists of three processes: pre-processing, initial lung field estimation and noise elimination. For the first step, the original images are resized and contrast enhanced. Then, each lung outlines are enhanced by shadow filter. The initial lung field estimation are obtained based on local thresholding, delete outer body regions, fill holes and filter regions from their property. However, noise has occurred in the result. To eliminate the noise, morphological operations techniques are used. To evaluate the performance, the proposed method was tested on a public JSRT dataset of 247 chest radiographs. The performance measures of proposed method (overlap, accuracy, sensitivity, specificity, precision, and F-score) are above 90%. The accuracy and overlap are 96.95% and 90.32% respectively with the average execution time of 18.68 s for 512 by 512 pixels resolutions. According to experimental results, our proposed method is unsupervised learning method, no training required and performed accurately.