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基于三维最小类内散度SVM的肺CT中的结节识别

, PP. 700-706

Keywords: 计算机辅助诊疗(CAD),多分类支持向量机,最小类内散度,三维矩阵

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Abstract:

提出一种基于三维类内散度的多分类支持向量机的肺部结节识别算法。首先设计可直接处理基于三维矩阵模式的输入样本的多分类SVM,并结合最小类内散度SVM,进一步提出基于三维最小类内散度的多分类SVM。该方法通过直接分析肺部候选结节的三维特征并继承最小类内散度SVM的优点,有效提高分类器的识别精度,降低假阳性。利用其它4种计算机辅助肺部结节检测算法及两位放疗师作为比较,对于来自吉林省肿瘤医院的200组临床病例进行实验,结果证明三维最小类内散度多分类SVM在计算机辅助肺部结节识别中的优越性。

References

[1]  Zhu Yanjie, Tan Yongqiang, Hua Yanqing, et al. Feature Selection and Performance Evaluation of Support Vector Machine-Based Classifier for Differentiating Benign and Malignant Pulmonary Nodules by Computed Tomography. Journal of Digital Imaging, 2010, 23(1): 51-65
[2]  Lee Y, Seo J B, Lee J G, et al. Performance Testing of Several Classifiers for Differentiating Obstructive Lung Diseases Based on Texture Analysis at High-Resolution Computerized Tomography. Computer Method and Programs in Biomedicine, 2009, 93(2): 206-215.
[3]  Gangeh M J, Srensen L, Shaker S B, et al. A Texton-Based Approach for the Classification of Lung Parenchyma in CT Images. Medical Image Computing and Computer-Assisted Intervention, 2010, 13(3): 595-602
[4]  Kim N, Seo J B, Lee Y, et al. Development of an Automatic Classification System for Differentiation of Obstructive Lung Disease Using HRCT.Journal of Digital Imaging, 2009, 22(2): 136-148
[5]  Ye Xujiong, Lin Xinyu, Dehmeshki J, et al. Shap-Based Computer-Aided Detection of Lung Nodules in Thoracic CT Images. IEEE Trans on Biomedical Engineering, 2009, 56(7): 1810-1820
[6]  Suzuki K, Armato S G Ⅲ, Li F, et al. Massive Training Artificial Neural Network for Detection of False Positives in Computerized Detection of Lung Nodules in Low-dose Computed Tomography. Medical Physics, 2003, 30(7): 1602-1617
[7]  Suzuki K, Shiraishi J, Abe H, et al. False-Positive Reduction in Computer-Aided Diagnostic Scheme for Detecting Nodules in Chest Radiographs by Means of Massive Training Artificial Neural Network. Academic Radiology, 2005, 12(2): 191-201
[8]  Suzuki K, Li F, Sone S, et al. Computer-Aided Diagnostic Scheme for Distinction between Benign and Malignant Nodules in Thoracic Low-dose CT by Use of Massive Training Artificial Neural Network. IEEE Trans on Medical Imaging, 2005, 24(9): 1138-1150
[9]  Suzuki K, Abe H, MacMahon H, et al. Image-Processing Technique for Suppressing Ribs in Chest Radiographs by Means of Massive Training Artificial Neural Network (MTANN). IEEE Trans on Medical Imaging,2006, 25(4): 406-416
[10]  Oda S, Awai K, Suzuki K,et al. Performance of Radiologists in Detection of Small Pulmonary Nodules on Chest Radiographs: Effect of Rib Suppression by the Massive Training Artificial Neural Network (MTANN) Technique on the Performance of Radiologists. American Journal of Roentgenology, 2009, 193: 397-402
[11]  Wang Qingzhu. Detection of Lung Nodules in CT Image Based on 3D SVMs. Ph.D Dissertation. Changchun, China: Jilin University, 2011 (in Chinese) (王青竹.基于三维SVMs的肺部CT中的结节检测算法.博士学位论文.长春:吉林大学, 2011)
[12]  Li Qiang, Sone S, Doi K. Selective Enhancement Filters for Nodules, Vessels, and Airway Walls in Two- and Three-Dimensional CT Scans.Medical Physics, 2003, 30(8): 2040-2051
[13]  Armato S G Ⅲ, Giger M L, Moran C J, et al. Computerized Detection of Pulmonary Nodules on CT Scans.Imaging Therapeutic Technology, 1999, 19(5): 1303-1311
[14]  Kyongtae T B, Jin-Sung K, Yong-Hum N, et al. Pulmonary Nodules: Automated Detection on CT Images with Morphologic Matching Algorithm-Preliminary Results. Radiology, 2005, 236: 286-293
[15]  Suykens J A K, Vandewalle J. Least Squares Support Vector Machine Classifiers. Neural Processing Letters, 1999, 9(3): 293-300
[16]  Wang Zhe, Chen Songcan. New Least Squares Support Vector Machines Based on Matrix Patterns.Neural Processing Letters, 2007, 26(1): 41-56
[17]  Duan K B, Rajapakse J C, Nguyen M N. One-versus-One and One-versus-All Multiclass SVM-RFE for Gene Selection in Cancer Classification // Proc of the 5th European Conference on Evolutionary Computation,Machine Learning and Data Mining in Bioinformatics. Valencia, Spain, 2007: 47-56
[18]  Suykens J A K, Vandewalle J. Multiclass Least Squares Support Vector Machines // Proc of the International Joint Conference on Neural Networks. Washington, USA, 1999: 900-903
[19]  Abdulkadir S. Multiclass Least-Squares Support Vector Machines for Analog Modulation Classification. Expert Systems with Applications, 2009, 36(3): 6681-6685
[20]  Zafeiriou S, Tefas A, Pitas I. Minimum Class Variance Support Vector Machines. IEEE Trans on Image Processing, 2007, 16(10): 2551-2564
[21]  de Lathauwer L, de Moor B, Vandewalle J. A Multilinear Singular Value Decomposition. SIAM Journal on Matrix Analysis and Application, 2000, 21(4): 1253-1278

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