Automated detection of pulmonary nodules in CT images with support vector machines
Many methods have been proposed to avoid radiologists fail to diagnose small pulmonary nodules. Recently, support vector machines (SVMs) had received an increasing attention for pattern recognition. In this paper, we present a computerized system aimed at pulmonary nodules detection; it identifies the lung field, extracts a set of candidate regions with a high sensitivity ratio and then classifies candidates by the use of SVMs. The Computer Aided Diagnosis (CAD) system presented in this paper supports the diagnosis of pulmonary nodules from Computed Tomography (CT) images as inflammation, tuberculoma, granuloma sclerosing hemangioma, and malignant tumor. Five texture feature sets were extracted for each lesion, while a genetic algorithm based feature selection method was applied to identify the most robust features. The selected feature set was fed into an ensemble of SVMs classifiers. The achieved classification performance was 100％, 92.75％ and 90.23％ in the training, validation and testing set, respectively. It is concluded that computerized analysis of medical images in combination with artificial intelligence can be used in clinical practice and may contribute to more efficient diagnosis.
CT Images automated detection computer aided diagnosis support vector machines classification
Liu Lu Liu Wanyu Sun Xiaoming
HIT-INSA Sino-French Research Center for Biomedical Imaging, Harbin Institute of TechnologyP. O. Box HIT-INSA Sino-French Research Center for Biomedical Imaging, Harbin Institute of Technology P. O. Bo
国际会议
第五届仪器科学与技术国际学术会议(ISIST 2008)Fifth International Symposium on Instrmentation Science and Technology
沈阳
英文
1-6
2008-09-15（万方平台首次上网日期，不代表论文的发表时间）