Lung cancer has become one of the malignant tumors with the fastest growing morbidity and mortality

bringing great threats to human health. Studies have shown that early detection of lung cancer accompanied with early treatment can improve the survival rate and prognostic conditions. Lung computed tomography (CT) images mainly include the lung parenchyma

air outside the lung parenchyma

and checking bed. Gray inhomogeneity always exists because the noise and bias field are strong in lung CT images

and the organizational structure is complex. Consequently

the lung parenchyma is difficult to effectively segmented and extracted in the field of auxiliary technology research for lung disease diagnosis. This study proposes an automatic segmentation algorithm based on superpixel refining segmentation combined with fuzzy c-means clustering to improve the accuracy of lung parenchyma segmentation. First

superpixel division is achieved

and refining segmentation is conducted on superpixel regions where an error occurs. Second

the fuzzy c-means clustering algorithm is used based on specific characteristics. Finally

the superpixel regions that share the same classification are merged

and the final segmentation results are obtained. The algorithm can use the gray and texture features of lung CT images. The spatial neighborhood information is introduced to enhance the space constraints for generating the correct superpixel classification

thereby effectively solving the problem of gray inhomogeneity. The algorithm can carry out the segmentation of lung parenchyma

remove the surrounding main blood vessels

and use morphological knowledge to remove branch blood vessels in the lungs. In clinical patients with four types of disease on the CT image data set with improved image characteristics

the lung parenchyma segmentation accuracy is increased by 0.8%. The algorithm accuracy is also increased to 99.46%. Experimental results show that the proposed algorithm can effectively overcome the interference of bias field and noise during image segmentation. Therefore

this algorithm can achieve the automatic refinement of lung parenchyma segmentation in lung CT image efficiently. The results are accurate and applicative. The algorithm has good robustness

and it is a fast

accurate

and effective automatic lung parenchyma segmentation method.