Comparative Study on Automatic Lung Parenchyma Segmentation of CT Data Using Improved OTSU and FCM Methods

Chen Jingjing; Zhao Dechun; Peng Chenglin; Wu Xiangxu

Abstract：Objective To compare two methods (improved OTSU and fuzzy c-means clustering) for segmentation of lung parenchyma based on CT images.Methods Two methods were analyzed and applied for the segmentation of 40 series of CT lung images.After segmentation,the lung parenchyma areas were segmented,the mediastinum and the trachea were removed.Finally,the subjective evaluation and objective analysis (consistency and information entropy) were used to evaluate the segmentation effect.Results It is demonstrated from the subjective results that the improved OTSU method decreased sharply the proportion of isolated pixel and increased smoothness of the edge of particles,while there was still internal clearance in the lung parenchyma.The lung parenchyma segmented by FCM algorithm was more complete with less holes,but with some adhesion.However in the extraction process,the main trachea was often segmented improperly into lung parenchyma,which would cause incomplete segmentation of the lung parenchyma.Objective analysis showed,in terms of consistency,the difference was not great and the areas segmented had a higher internal region consistency;for the information entropy,the segmentation result of the FCM method was better.Conclusion The experiment results show that for the CT images with a strong gray contrast between the target and background,the improved OTSU method works better in the segmentation of lung parenchyma.However,there are limitation adaptive threshold and running time that can be improved.While for the images with uncertainty and fuzziness,the FCM method is superior to the former.But as far as the segmentation accuracy and the anti-noise capability are concerned,this method needs further improvement.