M. Outtas, Lu Zhang, O. Déforges, W. Hamidouche, A. Serir
{"title":"肝脏超声图像无参考质量指标的评价","authors":"M. Outtas, Lu Zhang, O. Déforges, W. Hamidouche, A. Serir","doi":"10.1109/QoMEX.2018.8463299","DOIUrl":null,"url":null,"abstract":"Although assessing post-processed medical images is still done by radiologists (rather than computers), numerous algorithms dedicated to medical image processing are developed without taking into consideration the expert's perceived quality scores. In order to evaluate these algorithms, we study in this paper four No-Reference(NR) quality assessment metrics in terms of correlation with perceived scores of experts. These scores were obtained through subjective tests conducted on ultrasound (US) livers images. Results show that one NR metric among the four evaluated performs the best for assessing the quality of US images. However, further study is needed for the development of more suitable NR metrics.","PeriodicalId":6618,"journal":{"name":"2018 Tenth International Conference on Quality of Multimedia Experience (QoMEX)","volume":"38 1","pages":"1-3"},"PeriodicalIF":0.0000,"publicationDate":"2018-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Evaluation of No-reference quality metrics for Ultrasound liver images\",\"authors\":\"M. Outtas, Lu Zhang, O. Déforges, W. Hamidouche, A. Serir\",\"doi\":\"10.1109/QoMEX.2018.8463299\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Although assessing post-processed medical images is still done by radiologists (rather than computers), numerous algorithms dedicated to medical image processing are developed without taking into consideration the expert's perceived quality scores. In order to evaluate these algorithms, we study in this paper four No-Reference(NR) quality assessment metrics in terms of correlation with perceived scores of experts. These scores were obtained through subjective tests conducted on ultrasound (US) livers images. Results show that one NR metric among the four evaluated performs the best for assessing the quality of US images. However, further study is needed for the development of more suitable NR metrics.\",\"PeriodicalId\":6618,\"journal\":{\"name\":\"2018 Tenth International Conference on Quality of Multimedia Experience (QoMEX)\",\"volume\":\"38 1\",\"pages\":\"1-3\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-05-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 Tenth International Conference on Quality of Multimedia Experience (QoMEX)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/QoMEX.2018.8463299\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 Tenth International Conference on Quality of Multimedia Experience (QoMEX)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/QoMEX.2018.8463299","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Evaluation of No-reference quality metrics for Ultrasound liver images
Although assessing post-processed medical images is still done by radiologists (rather than computers), numerous algorithms dedicated to medical image processing are developed without taking into consideration the expert's perceived quality scores. In order to evaluate these algorithms, we study in this paper four No-Reference(NR) quality assessment metrics in terms of correlation with perceived scores of experts. These scores were obtained through subjective tests conducted on ultrasound (US) livers images. Results show that one NR metric among the four evaluated performs the best for assessing the quality of US images. However, further study is needed for the development of more suitable NR metrics.