Automated extraction of lung cancer lesions from multislice chest CT images by using three-dimensional image processing

This paper discusses the test for the automatic extraction of lung cancer candidate areas from the high-resolution continuous chest CT image taken with the slice interval of 1 mm, by a three-dimensional image processing technique. The detection of local suspicious regions (SR) is considered. A procedure is developed which discriminates the lung area from other images (mostly vessels) based on the differences of three-dimensional shapes. The actual procedure in the processing is composed largely of two steps: (1) segmentation of lung area; and (2) SR extraction. In this study, the processing is realized by a relatively simple procedure, combining such basic processes as thresholding, fusion of three-dimensional figures (or three-dimensional distance transformation) and the three-dimensional skeletonization. The usefulness of the method is demonstrated using an actual case of lung cancer. The study is significant as the first test that demonstrated the usefulness of the three-dimensional image processing, using the three-dimensional CT image of a practically sufficient accuracy.