吉林大学学报(理学版)

• 计算机科学 • 上一篇    下一篇

基于空间像素强度的脑瘤图像检索方法

李清亮1, 张子鹏1, 时玮淞2, 蒋振刚1, 赵家石1, 师为礼1   

  1. 1. 长春理工大学 计算机科学技术学院 , 长春 130022; 2. 长春理工大学 光电工程学院, 长春 130022
  • 收稿日期:2017-04-24 出版日期:2018-05-26 发布日期:2018-05-18
  • 通讯作者: 师为礼 E-mail:shiweili@cust.edu.cn

Brain Tumor Images Retrieval Method Based onSpatial Pixel Intensity

LI Qingliang1, ZHANG Zipeng1, SHI Weisong2,JIANG Zhengang1, ZHAO Jiashi1, SHI Weili1   

  1. 1. School of Computer Science and Technology, Changchun University ofScience and Technology, Changchun 130022, China; 2. School of Electro\|Optical Engineering,Changchun University of Science and Technology, Changchun 130022, China
  • Received:2017-04-24 Online:2018-05-26 Published:2018-05-18
  • Contact: SHI Weili E-mail:shiweili@cust.edu.cn

摘要: 针对脑瘤图像提出一种基于内容的检索方法. 首先采用图像膨胀算法增强脑瘤图像的感兴趣区域(ROI); 然后基于点的空间像素强度的特征提取方法, 描述脑瘤图像增强后的ROI局部特征; 最后引入聚合特征算法提高图像特征的辨别能力和压缩能力. 实验对比3种脑瘤图象检索方法的结果表明, 该算法可得到较高的检索精确度, 可实现高效、 准确的脑瘤图像检索.

关键词: 基于内容图像检索, 聚合特征, 脑瘤图像检索, 词袋模型, 空间像素强度

Abstract: In view of brain tumor images, we proposed a retrieval method based on content. Firstly, we augmented the region of interest (ROI) of brain tumor images by using the image expansion algorithm. Secondly, based on the feature extraction method of pointbased spatial pixel intensity, we described the local characteristics of the augmented ROI. Finally, we introduced the aggregated feature algorithm to improve the discrimination and compression ability of image features. The results of the experimental comparison of three brain tumor image retrieval methods show that the algorithm can obtain high retrieval accuracy and prove that the algorithm can achieve efficient and accurate brain tumor image retrieval.

Key words: brain tumor image retrieval, aggregated feature, spatial pixel intensity, contentbased image retrieval, bagofwords model

中图分类号: 

  • TP391