Sascha Hein , Omnia Saleh , Changjun Li , Julian Nold , Stephen Westland
{"title":"用改进的色差方程连接仪器和视觉感知:多中心研究","authors":"Sascha Hein , Omnia Saleh , Changjun Li , Julian Nold , Stephen Westland","doi":"10.1016/j.dental.2024.07.003","DOIUrl":null,"url":null,"abstract":"<div><h3>Objectives</h3><p>This multicenter study aimed to evaluate visual-instrumental agreement of six color measurement devices and optimize three color difference equations using a dataset of visual color differences (∆V) from expert observers.</p></div><div><h3>Methods</h3><p>A total of 154 expert observers from 16 sites across 5 countries participated, providing visual scaling on 26 sample pairs of artificial teeth using magnitude estimation. Three color difference equations (Δ<em>E</em>*<sub>ab</sub>, ∆<em>E</em><sub>00</sub>, and CAM16-UCS) were tested. Optimization of all three equations was performed using device-specific weights, and the standardized residual sum of squares (<em>STRESS</em>) index was used to evaluate visual-instrumental agreement.</p></div><div><h3>Results</h3><p>The Δ<em>E</em>*<sub>ab</sub> formula exhibited <em>STRESS</em> values from 18 to 40, with visual-instrumental agreement between 60 % and 82 %. The ∆<em>E</em><sub>00</sub> formula showed <em>STRESS</em> values from 26 to 32, representing visual-instrumental agreement of 68 % to 74 %. CAM16-UCS demonstrated <em>STRESS</em> values from 32 – 39, with visual-instrumental agreement between 61–68 %. Following optimization, <em>STRESS</em> values decreased for all three formulas, with Δ<em>E</em><sup>’</sup> demonstrating average visual-instrumental agreement of 79 % and ∆<em>E</em><sub>00</sub> of 78 %. CAM16-UCS showed average visual-instrumental agreement of 76 % post optimization.</p></div><div><h3>Significance</h3><p>Optimization of color difference equations notably improved visual-instrumental agreement, overshadowing device performance. The optimzed Δ<em>E</em><sup>’</sup> formula demonstrated the best overall performance combining computational simplicty with outstanding visual-instrumental agreement.</p></div>","PeriodicalId":298,"journal":{"name":"Dental Materials","volume":"40 10","pages":"Pages 1497-1506"},"PeriodicalIF":4.6000,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0109564124002045/pdfft?md5=5c7d5072aa729b2acffbfd2bc78098fb&pid=1-s2.0-S0109564124002045-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Bridging instrumental and visual perception with improved color difference equations: A multi-center study\",\"authors\":\"Sascha Hein , Omnia Saleh , Changjun Li , Julian Nold , Stephen Westland\",\"doi\":\"10.1016/j.dental.2024.07.003\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Objectives</h3><p>This multicenter study aimed to evaluate visual-instrumental agreement of six color measurement devices and optimize three color difference equations using a dataset of visual color differences (∆V) from expert observers.</p></div><div><h3>Methods</h3><p>A total of 154 expert observers from 16 sites across 5 countries participated, providing visual scaling on 26 sample pairs of artificial teeth using magnitude estimation. Three color difference equations (Δ<em>E</em>*<sub>ab</sub>, ∆<em>E</em><sub>00</sub>, and CAM16-UCS) were tested. Optimization of all three equations was performed using device-specific weights, and the standardized residual sum of squares (<em>STRESS</em>) index was used to evaluate visual-instrumental agreement.</p></div><div><h3>Results</h3><p>The Δ<em>E</em>*<sub>ab</sub> formula exhibited <em>STRESS</em> values from 18 to 40, with visual-instrumental agreement between 60 % and 82 %. The ∆<em>E</em><sub>00</sub> formula showed <em>STRESS</em> values from 26 to 32, representing visual-instrumental agreement of 68 % to 74 %. CAM16-UCS demonstrated <em>STRESS</em> values from 32 – 39, with visual-instrumental agreement between 61–68 %. Following optimization, <em>STRESS</em> values decreased for all three formulas, with Δ<em>E</em><sup>’</sup> demonstrating average visual-instrumental agreement of 79 % and ∆<em>E</em><sub>00</sub> of 78 %. CAM16-UCS showed average visual-instrumental agreement of 76 % post optimization.</p></div><div><h3>Significance</h3><p>Optimization of color difference equations notably improved visual-instrumental agreement, overshadowing device performance. The optimzed Δ<em>E</em><sup>’</sup> formula demonstrated the best overall performance combining computational simplicty with outstanding visual-instrumental agreement.</p></div>\",\"PeriodicalId\":298,\"journal\":{\"name\":\"Dental Materials\",\"volume\":\"40 10\",\"pages\":\"Pages 1497-1506\"},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2024-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S0109564124002045/pdfft?md5=5c7d5072aa729b2acffbfd2bc78098fb&pid=1-s2.0-S0109564124002045-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Dental Materials\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0109564124002045\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"DENTISTRY, ORAL SURGERY & MEDICINE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Dental Materials","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0109564124002045","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"DENTISTRY, ORAL SURGERY & MEDICINE","Score":null,"Total":0}
Bridging instrumental and visual perception with improved color difference equations: A multi-center study
Objectives
This multicenter study aimed to evaluate visual-instrumental agreement of six color measurement devices and optimize three color difference equations using a dataset of visual color differences (∆V) from expert observers.
Methods
A total of 154 expert observers from 16 sites across 5 countries participated, providing visual scaling on 26 sample pairs of artificial teeth using magnitude estimation. Three color difference equations (ΔE*ab, ∆E00, and CAM16-UCS) were tested. Optimization of all three equations was performed using device-specific weights, and the standardized residual sum of squares (STRESS) index was used to evaluate visual-instrumental agreement.
Results
The ΔE*ab formula exhibited STRESS values from 18 to 40, with visual-instrumental agreement between 60 % and 82 %. The ∆E00 formula showed STRESS values from 26 to 32, representing visual-instrumental agreement of 68 % to 74 %. CAM16-UCS demonstrated STRESS values from 32 – 39, with visual-instrumental agreement between 61–68 %. Following optimization, STRESS values decreased for all three formulas, with ΔE’ demonstrating average visual-instrumental agreement of 79 % and ∆E00 of 78 %. CAM16-UCS showed average visual-instrumental agreement of 76 % post optimization.
Significance
Optimization of color difference equations notably improved visual-instrumental agreement, overshadowing device performance. The optimzed ΔE’ formula demonstrated the best overall performance combining computational simplicty with outstanding visual-instrumental agreement.
期刊介绍:
Dental Materials publishes original research, review articles, and short communications.
Academy of Dental Materials members click here to register for free access to Dental Materials online.
The principal aim of Dental Materials is to promote rapid communication of scientific information between academia, industry, and the dental practitioner. Original Manuscripts on clinical and laboratory research of basic and applied character which focus on the properties or performance of dental materials or the reaction of host tissues to materials are given priority publication. Other acceptable topics include application technology in clinical dentistry and dental laboratory technology.
Comprehensive reviews and editorial commentaries on pertinent subjects will be considered.