{"title":"基于共现、行程长度和粗糙度特征的组织图像检索系统","authors":"Loay E. George, Esraa Z. Mohammed","doi":"10.1109/ICCMA.2013.6506186","DOIUrl":null,"url":null,"abstract":"The research presented in this paper was aimed to improve the retrieval performance of an images retrieval system in medical applications based on texture features. In general, the work consists of two phases: (1) enrollment phase, which consist of feature extraction based on Co-occurrence matrix and run length matrix features combined with developed method to measure the roughness, (2) retrieving phase, which use the artificial neural network and similarity measurement. The conducted tests were carried on 600 medical images from four types of tissues (i.e., blood cells, breast tissues, GI tissues, liver tissues) and give very high precision and recall rates (100,98).","PeriodicalId":187834,"journal":{"name":"2013 International Conference on Computer Medical Applications (ICCMA)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Tissues image retrieval system based on co-occuerrence, run length and roughness features\",\"authors\":\"Loay E. George, Esraa Z. Mohammed\",\"doi\":\"10.1109/ICCMA.2013.6506186\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The research presented in this paper was aimed to improve the retrieval performance of an images retrieval system in medical applications based on texture features. In general, the work consists of two phases: (1) enrollment phase, which consist of feature extraction based on Co-occurrence matrix and run length matrix features combined with developed method to measure the roughness, (2) retrieving phase, which use the artificial neural network and similarity measurement. The conducted tests were carried on 600 medical images from four types of tissues (i.e., blood cells, breast tissues, GI tissues, liver tissues) and give very high precision and recall rates (100,98).\",\"PeriodicalId\":187834,\"journal\":{\"name\":\"2013 International Conference on Computer Medical Applications (ICCMA)\",\"volume\":\"13 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 International Conference on Computer Medical Applications (ICCMA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCMA.2013.6506186\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 International Conference on Computer Medical Applications (ICCMA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCMA.2013.6506186","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Tissues image retrieval system based on co-occuerrence, run length and roughness features
The research presented in this paper was aimed to improve the retrieval performance of an images retrieval system in medical applications based on texture features. In general, the work consists of two phases: (1) enrollment phase, which consist of feature extraction based on Co-occurrence matrix and run length matrix features combined with developed method to measure the roughness, (2) retrieving phase, which use the artificial neural network and similarity measurement. The conducted tests were carried on 600 medical images from four types of tissues (i.e., blood cells, breast tissues, GI tissues, liver tissues) and give very high precision and recall rates (100,98).