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Research on automatic identification technique of CT image in lung

Zhao Zhijie (School of Computer and Information Engineering, Harbin University of Commerce, Harbin, China + Key Laboratory of Electronic Commerce and Information Processing of Heilongjiang Province)
Ren Cong (School of Computer and Information Engineering, Harbin University of Commerce, Harbin, China + Key Laboratory of Electronic Commerce and Information Processing of Heilongjiang Province)
Sun Huadong (School of Computer and Information Engineering, Harbin University of Commerce, Harbin, China + Key Laboratory of Electronic Commerce and Information Processing of Heilongjiang Province)
Fan Zhipeng (School of Computer and Information Engineering, Harbin University of Commerce, Harbin, China + Key Laboratory of Electronic Commerce and Information Processing of Heilongjiang Province)
Gao Ze (School of Computer and Information Engineering, Harbin University of Commerce, Harbin, China + Key Laboratory of Electronic Commerce and Information Processing of Heilongjiang Province)

Lung cancer has become the world's human cancer disease in the "first killer." In this paper, three aspects of lung CT images were treated. Firstly, based on the CT image preprocessing, the lung parenchyma was segmented by random walk algorithm and the ROI was extracted from the pulmonary parenchyma; Secondly, the 10-dimensional feature vectors of pulmonary nodule ROI were extracted by the gray level co-occurrence matrix algorithm; Finally, support vector machine as a classifier is to identify the pulmonary nodules and the accuracy rate is more than 94%. The experimental results show that the study of automatic CT image recognition can provide some data reference for doctors and play a supporting role in the course of treatment.

Keywords: CT image, image segmentation, ROI extraction, feature extraction, support vector machine