Abstract
Lung field segmentation in chest radiograph is an essential and important step for automatically analyzing x-ray image. There are various supervised methods that perform well. But the acquisition of training data is time-consuming and requires great effort for the clinicians of the Hospital in Taiwan. Hence, we present an unsupervised method based on multiresolution fractal feature vector. The feature vector consists of M-band wavelet transform and fractal geometry representation. Such a multiresolution fractal feature vector has been proved trustworthy in the application of texture images and liver ultrasound images. With the robust feature vector, the difference between lung field region and non-lung field region can be distinguished by simple unsupervised clustering. But the clustering result from multiresilution feature vector is rough and ragged, so we apply other segmentation methods to complete the unsupervised segmentation. We apply graph cut segmentation with anatomy information to obtain a regional initial lung field contour without the interference of clavicle edges. In the end, the final contour is obtained by anatomy-based active contour model. We do some refinements for active contour model in order to fit the boundary of lung field better, especially at the bottom of lung field contour. In the experimental results, we apply our method on real case images and JSRT database with ground truth (SCR database). The results from real case show the feasibility of our method. And the performance of images from JSRT database is comparative to other unsupervised segmentation methods which were proposed two years ago. Deserved to be mentioned, there are some methods based on active contour model. But the acquisition of initial contour in our method is more robust than other methods which are based on rule-based method.