Comprehensive Review of Automatic Lung Segmentation Techniques on Pulmonary CT Images

Segmentation is the process of partitioning an image into distinctive subsets that share similar characteristics. Segmentation is an important prerequisite to semantic image analysis. Segmentation in general is useful in many different applications such as object and face detection and recognition. Particularly in medical image analysis segmentation plays a vital role in efficient processing of images. Segmentation is used to determine the volume of mass, planning of radiotherapy, and detection of artifacts in various organs. In lung cancer diagnosis, segmentation of lungs is the crucial step. Segmenting lungs from nearby structures significantly reduce the execution time of nodule detection and helps improve its efficiency. Lung segmentation is challenging and difficult task considering the heterogeneous nature of lung fields, closeness in gray level of different soft tissues, anatomical variability, and differences in scanners and scanning protocols and dose of radiation. Various automatic and semi-automatic approaches are presented for lung or nodule segmentation. The proposed study is a review of numerous techniques for lung segmentation. The present work investigated lung segmentation methods starting with conventional methods to machine learning techniques and finally the most remarkable methods of deep learning.