摘要
为实现在线生物文献磁共振成像(MRI)图像库的构建,利用图像特征的塔式梯度方向直方图(PHOG)和塔式关键词直方图(PHOW)进行互补特征表示,使用支持向量机对MRI图像与非MRI图像以及脑部MRI与非脑部MRI图像进行自动分类。实验结果表明,空间形状信息与局部分布信息融合的特征能提高图像分类的准确率,为构建在线文献中MRI图像库的知识系统提供技术支持。
In order to construct the Magnetic Resonance Imaging（MRI） database from online literature, MRI image recognition and brain MRI recognition are studied. In this paper, two complementary features, Pyramid Histogram of Orientated Gradients（PHOG） and Pyramid Histogram of Words（PHOW） are adopted to extract and describe the features of images. An improved Support Vector Machine（SVM） classifier based on feature fusion which combines spatial shape and local distribution information is proposed. Experimental result shows a significant improvement in the average accuracy of the fusion classifier as compared with classifiers only based on PHOG or PHOW. It provides a foundation of building a knowledge base system that can interpret MRI images in online articles.
出处
《计算机工程》
CAS
CSCD
北大核心
2011年第15期1-5,共5页
Computer Engineering
基金
浙江省自然科学基金资助项目(Y1101359)
关键词
在线文献
磁共振成像
塔式梯度方向直方图
塔式关键词直方图
图像分类
online literature
Magnetic Resonance Imaging（MRI）
Pyramid Histogram of Orientated Gradients（PHOG）
Pyramid Histogram of Words（PHOW）
image
