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SUMMARYWith the recent development of multislice CT, it is now possible to derive 3D cross-sectional images for the whole lung region with high accuracy in a short time. In particular, with the drastic improvement of resolution in the axial direction, it is expected that 3D image analysis will play a significant role. This paper discusses an algorithm that extracts the pulmonary fissures from multislice CT images. The fissure is a feature which is clinically important for the identification of lung sections. The fissures have a very thin film structure. The proposed method extracts the lung fissure by identification of the region containing the fissure and emphasis of the fissure. The lung blood vessels are classified for each lobe, and the region containing the fissure is identified on the basis of the 3D distance from the lung blood vessel. Then, region growing is applied on the basis of the normal vector of the emphasized sheet shadow, and the fissure is extracted. The proposed method is applied to clinical images of 20 cases, and its accuracy is evaluated, demonstrating the effectiveness of the method.

Three-dimensional computer-aided diagnosis of the internal structure of SPNs using CE dynamic HCT was found to be effective for differentiating between BNs and MNs.

Abnormal CT and spirometric parameters suggestive of COPD and ILD were strong risk factors for lung cancer, even after adjusting for gender, age and smoking status.