基于改进的SVM的甲状腺图像检索

任小康，白勇峰，范丽，李颜瑞

doi:10.3969/j.issn.1007130X.2011.

计算机工程与科学 >

2011 , Vol. 33 >Issue 1: 127 - 131

DOI: https://doi.org/10.3969/j.issn.1007130X.2011.

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基于改进的SVM的甲状腺图像检索

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(西北师范大学数学与信息学院，甘肃兰州 730070)

任小康(1963),男,辽宁大连人,硕士,教授,研究方向为多媒体信息处理、模式识别和数字水印。白勇峰(1980),男,宁夏银川人,硕士生,研究方向为多媒体信息处理、模式识别和数字水印。范丽(1978),女,辽宁鞍山人,硕士生,研究方向为数字水印、计算机视觉和模式识别。李颜瑞(1982),男,山西长治人,硕士生,研究方向为多媒体信息处理、模式识别和数字水印。

收稿日期: 2010-05-20

修回日期: 2010-09-03

网络出版日期: 2011-01-25

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A Thyroid Image Retrieval Algorithm Based on Improved SVMs

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（School of Mathematics and Information Technology,Northwest Normal University,Lanzhou 730070,China）

Received date: 2010-05-20

Revised date: 2010-09-03

Online published: 2011-01-25

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摘要

针对SVM处理大数据量和区分训练集样本属性的重要性差的问题，我们将SVM和粗糙集结合，构造了基于粗糙集与SVM的图像检索相关反馈算法，将其应用于甲状腺CT图像检索。实验结果表明，改进的SVM分类精度可达到92.53%，相比SVM的分类精度（76.58%）提高了15.95%，进而使检索的查准率和查全率也分别提高到89.53%和29.67%。

关键词： 图像检索; 粗糙集; 支持向量机; 相关反馈; 甲状腺CT图像

本文引用格式

任小康，白勇峰，范丽，李颜瑞 . 基于改进的SVM的甲状腺图像检索[J]. 计算机工程与科学, 2011 , 33(1) : 127 -131 . DOI: 10.3969/j.issn.1007130X.2011.

Abstract

In allusion to SVM,s defects of handling large amount of data and distinguishing the importance of the training set,this paper joins the SVM classifier with the rough sets theory, and constructs an improved image feedback retrieval algorithm based on rough sets and SVMs,which are used to retrieve thyroid CT images .The results show that the improved SVM classifier can get 92.53% accuracy which is about 15.95% higher than 76.58% using SVM,and the retrieval of poor accuracy and recallprecision are also increasedr by 89.53% and 29.67%.

Key words： image retrieval;rough sets;SVM;relevance feedback; thyroid CT images

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