Hsin-Chen Chen, Tai-Hua Yang, A. Thoreson, Chunfeng Zhao, P. Amadio, F. Su, Wenyan Jia, Yung-Nien Sun, K. An, Mingui Sun
{"title":"Multiresolution Image Analysis for Automatic Quantification of Collagen Gel Contraction","authors":"Hsin-Chen Chen, Tai-Hua Yang, A. Thoreson, Chunfeng Zhao, P. Amadio, F. Su, Wenyan Jia, Yung-Nien Sun, K. An, Mingui Sun","doi":"10.1109/NEBEC.2013.115","DOIUrl":null,"url":null,"abstract":"Quantifying collagen gel contraction is important in tissue engineering and biological research because it provides spatial-temporal assessments of cell behaviors and tissue material properties. However, these assessments currently rely on manual processing, which is time-consuming and subjective to personal opinions. We present a multiresolution image analysis system for automatic quantification of gel contraction. This system includes a color conversion process to normalize and enhance the contrast between gel and background. Then, a deformable circular model is activated automatically to capture details of gel boundaries. These steps are coordinated by a multiresolution strategy. The target measurements are obtained after gel segmentation. Our experiments using 30 images demonstrated a high consistency between the proposed and manual segmentation methods. The system can process large-size images (4000×3000) at a rate of approximately one second per image. It thus serves as a useful tool for analyzing large biological and biomaterial imaging datasets efficiently and objectively.","PeriodicalId":153112,"journal":{"name":"2013 39th Annual Northeast Bioengineering Conference","volume":"96 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 39th Annual Northeast Bioengineering Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NEBEC.2013.115","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Quantifying collagen gel contraction is important in tissue engineering and biological research because it provides spatial-temporal assessments of cell behaviors and tissue material properties. However, these assessments currently rely on manual processing, which is time-consuming and subjective to personal opinions. We present a multiresolution image analysis system for automatic quantification of gel contraction. This system includes a color conversion process to normalize and enhance the contrast between gel and background. Then, a deformable circular model is activated automatically to capture details of gel boundaries. These steps are coordinated by a multiresolution strategy. The target measurements are obtained after gel segmentation. Our experiments using 30 images demonstrated a high consistency between the proposed and manual segmentation methods. The system can process large-size images (4000×3000) at a rate of approximately one second per image. It thus serves as a useful tool for analyzing large biological and biomaterial imaging datasets efficiently and objectively.