三维隐SVM算法设计及在胸CT图像病灶检测中的应用

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Abstract Accuracy of Computer Aided Detection (CAD) of lung lesions in chest CT may be affected by irregular shapes and simple texture of the lesions. To improve the poor performance of current CAD schemes,relative position from the suspected lesion to the whole lung area is added as a latent variable on the basis of traditional texture and shape features,which also participates in optimizing the SVM. Furthermore,considering 3D feature of the lung lesions,3D matrixes based SVM (3D SVM) is combined into the Latent SVM (L-SVM) to design 3D SVM with latent variables (3D-L-SVM). 150-case database from Jilin Tumor Hospital is used to validate the proposed algorithm. The performances of other three CAD schemes are compared on the same database. True positives of the 3D-L-SVM achieves 97.5% with the false positives of 9.21%. The experimental results verify the advantages of the proposed algorithm and effectiveness of assisting the radiologists.

Key words： Computer-Aided Diagnosis 3D Matrix Latent SVM

Received: 13 August 2012

ZTFLH:	TP181
	TP391

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	Articles by authors
	WANG Qing-Zhu
	KANG Wen-Wei
	WANG Bin

Cite this article:

WANG Qing-Zhu,KANG Wen-Wei,WANG Bin. Design of 3D Latent-SVM and Application to Detection of Lesions in Chest CT[J]. , 2013, 26(5): 460-466.

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http://manu46.magtech.com.cn/Jweb_prai/EN/ OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2013/V26/I5/460

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