Automatic lung segmentation for large-scale medical image management

Digital medical images assist specialists in improving their diagnostic efficiency and in treating diseases. For example, the chest Computed Tomography (CT) images help in diagnosing the lung disease. The chest CT scan generates multiple images of a patient's lung. The size of the medical imaging data has increased with the usage of medical images. In a picture archiving and communication system, large-scale medical images must be transmitted to specialists through either wired or wireless communications and retained in the archive. Hence, medical images have to be compressed, and there should be no damage to the Region of Interest (RoI) during compression. In order to protect the RoI, image segmentation is needed to detect RoI in medical images. Among the various image segmentation methods available, the method using Level-set is robust to irregular noises. However, the problems faced in using this method include manual input of the initial contour and slow performance speed. Inputting an initial contour to the Level-set that correctly fits the object's form helps in reducing the number of repetitions. This in turn helps in improving the segmentation performance speed. However, it is difficult for a user to input an appropriate initial contour. Therefore, this paper aims at providing a method to auto-configure the initial contour in the Level-set method. Multi-resolution analysis helps in reducing the pace of the auto-configuration process of the initial contour. In addition, the volume data of a CT image is used to prevent data loss that occurs during the MRA transformation process. Studies have confirmed that the proposed method facilitates drastic improve.