Abstract

Info

Author(s):: TANG Siyuan1; LIU Yanru2; YANG Min1; XU Ruiying1; 1. Department of Computer Science and Technology, Baotou Medical College of Inner Mongolia University of Science & Technology, Baotou 014040, China; 2. Department of Medical Technology, Baotou Medical College of Inner Mongolia University of Science & Technology, Baotou 014040, China

Keywords:: Keywords: pulmonary nodule; CT image; region growing method; multi-scale Gaussian filter; fuzzy C-means clustering; support vector machine classifier

Abstract:: Abstract: Objective To detect and identify pulmonary nodules from thoracic regions with background and noise. Methods After DICOM-format medical images were converted into JPG images, region growing method was applied to segment lung parenchyma and remove interference information outside lung area. Subsequently, multi-scale Gaussian filter was used to enhance images, and fuzzy C-means clustering algorithm was applied to extract regions of interest of pulmonary nodules. Finally, the features of pulmonary nodules were extracted and normalized, and the pulmonary nodules were identified and marked with support vector machine classifier. Results For the random sample of 120 images, the detection rate of pulmonary nodules reached 92.3% and the accuracy rate of the classification and recognition of pulmonary nodules was up to 95.6%. The experimental results revealed that using the proposed method could effectively eliminate the disturbances from crossing- and strip-shaped blood vessels and other disturbances, realizing an accurate detection and recognition of pulmonary nodules. Conclusion Using the proposed method can not only achieve an accurate detection and recognition of pulmonary nodules, but also reduce misjudgment rate. Moreover, the proposed algorithm has a better convergence.