基于三维最小类内散度SVM的肺CT中的结节识别

Abstract
Figure/Table
References
Related Citation (1)

Download: PDF (447 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract Multi-class Support Vector Machines (MC-SVM) based on 3 Dimension (3D) minimum within-class scatter is presented. MC-SVM based on 3D matrix patterns (MC-SVM^3Dmatrix) is proposed firstly which operates inputs as 3D matrixes directly, and minimum within-class scatter SVM is adopted to design a 3D minimum within-class scatter MC-SVM. Taking advantages of both minimum within-class scatter SVM and feature of 3D space, the algorithm improves accuracy of classifiers and reduces False Positives (FP) effectively. 200-case database from Jilin Tumor Hospital is used to validate the proposed algorithm. The performances of other four CAD schemes, two radiologists and the proposed algorithm are compared on the same database. The experimental results verify the effectiveness of the proposed algorithm.

Key words： Computer-Aided Diagnosis (CAD) Multi-Class SVMs Minimum Within-Class Scatter 3D Matrix

Received: 23 February 2011

ZTFLH:	TP181
	TP391

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	WANG Qing-Zhu
	KANG Wen-Wei
	WANG Xin-Zhu
	Wang Bin

Cite this article:

WANG Qing-Zhu,KANG Wen-Wei,WANG Xin-Zhu等. Identification of Lung Nodules in CT Images Based on 3D Minimum Within-Class Scatter SVM[J]. , 2011, 24(5): 700-706.

URL:

http://manu46.magtech.com.cn/Jweb_prai/EN/ OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2011/V24/I5/700

[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