Automatic fissure detection in CT images based on the genetic algorithm

Lung cancer is one of the most frequently occurring cancer and has a very low five-year survival rate. Computer-aided diagnosis (CAD) helps reducing the burden of radiologists and improving the accuracy of abnormality detection during CT image interpretations. Owing to rapid development of the scanner technology, the volume of medical imaging data is becoming huger and huger. Automated segmentations of the target organ region are always required by the CAD systems. Although the analysis of lung fissures provides important information for treatment, it is still a challenge to extract fissures automatically based on the CT values because the appearance of lung fissures is very fuzzy and indefinite. Since the oblique fissures can be visualized more easily among other fissures on the chest CT images, they are used to check the exact localization of the lesions. In this paper, we propose a fully automatic fissure detection method based on the genetic algorithm to identify the oblique fissures. The accurate rates of identifying the oblique fissures in the right lung and the left lung are 97% and 86%, respectively when the method was tested on 87 slices.