{"title":"基于权重自动设置的医学图像检索算法","authors":"Qidong Zhang, Liqun Gao","doi":"10.1109/ICINIS.2010.173","DOIUrl":null,"url":null,"abstract":"Integrating multiple features content-based image retrieval can overcome the problems of single feature, but how to combine these features and feature representation methods is important in image retrieval. In this paper an image feature retrieval setting up weight automatically based on particle swarm optimization algorithm is proposed, which could guide the movement direction for particles, and close to ideal solution set quickly. Considering the characteristics of medical image ,, the contour let transform is used for texture feature extraction. Zernike moments extracts shape feature. The experimental results show that the recall and precision of this proposed approach is better. It can get the best feature combination for medical image retrieval.","PeriodicalId":319379,"journal":{"name":"2010 Third International Conference on Intelligent Networks and Intelligent Systems","volume":"6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Medical Image Retrieval Algorithm Using Setting Up Weight Automatically\",\"authors\":\"Qidong Zhang, Liqun Gao\",\"doi\":\"10.1109/ICINIS.2010.173\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Integrating multiple features content-based image retrieval can overcome the problems of single feature, but how to combine these features and feature representation methods is important in image retrieval. In this paper an image feature retrieval setting up weight automatically based on particle swarm optimization algorithm is proposed, which could guide the movement direction for particles, and close to ideal solution set quickly. Considering the characteristics of medical image ,, the contour let transform is used for texture feature extraction. Zernike moments extracts shape feature. The experimental results show that the recall and precision of this proposed approach is better. It can get the best feature combination for medical image retrieval.\",\"PeriodicalId\":319379,\"journal\":{\"name\":\"2010 Third International Conference on Intelligent Networks and Intelligent Systems\",\"volume\":\"6 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 Third International Conference on Intelligent Networks and Intelligent Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICINIS.2010.173\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 Third International Conference on Intelligent Networks and Intelligent Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICINIS.2010.173","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Medical Image Retrieval Algorithm Using Setting Up Weight Automatically
Integrating multiple features content-based image retrieval can overcome the problems of single feature, but how to combine these features and feature representation methods is important in image retrieval. In this paper an image feature retrieval setting up weight automatically based on particle swarm optimization algorithm is proposed, which could guide the movement direction for particles, and close to ideal solution set quickly. Considering the characteristics of medical image ,, the contour let transform is used for texture feature extraction. Zernike moments extracts shape feature. The experimental results show that the recall and precision of this proposed approach is better. It can get the best feature combination for medical image retrieval.