基于三维最小类内散度SVM的肺CT中的结节识别  

Identification of Lung Nodules in CT Images Based on 3D Minimum Within-Class Scatter SVM

作　　者：王青竹[1,2] 康文炜[2] 王新竹[2] 王斌[3] 

机构地区：[1]东北电力大学信息工程学院,吉林132012 [2]吉林大学通信工程学院,长春130025 [3]吉林省肿瘤医院腹科,长春130012

出　　处：《模式识别与人工智能》2011年第5期700-706,共7页Pattern Recognition and Artificial Intelligence

基　　金：吉林省教育厅"十一五"科学技术研究项目(No.2010360)资助

摘　　要：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.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-SVM3Dmatrix） 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.

关 键 词：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 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]