L. Iannucci, L. Lombardo, M. Parvis, P. Cristiani, R. Basséguy, E. Angelini, S. Grassini
{"title":"An imaging system for microbial corrosion analysis","authors":"L. Iannucci, L. Lombardo, M. Parvis, P. Cristiani, R. Basséguy, E. Angelini, S. Grassini","doi":"10.1109/I2MTC.2019.8826965","DOIUrl":null,"url":null,"abstract":"This paper describes a viable and self-contained imaging system able to assess and quantify the effects of microbial corrosion on metals surface. The proposed image processing uses Scanning Electron Microscope micrographs to analyze bacteria attachment on sample surface and to estimate the degradation degree of the material. After a preliminary brightness and contrast normalization, which refines the image taken by the operator, the software is able to identify dark spots on the clear metal surface. These are then attributed to singly attached bacteria or to larger clusters, which are the most dangerous ones, as they could overlay corrosion pits. After that, the degradation of the material is evaluated through the quantification of microbial attachment on the surface and through dimensional distribution of bacteria clusters.","PeriodicalId":132588,"journal":{"name":"2019 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/I2MTC.2019.8826965","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8
Abstract
This paper describes a viable and self-contained imaging system able to assess and quantify the effects of microbial corrosion on metals surface. The proposed image processing uses Scanning Electron Microscope micrographs to analyze bacteria attachment on sample surface and to estimate the degradation degree of the material. After a preliminary brightness and contrast normalization, which refines the image taken by the operator, the software is able to identify dark spots on the clear metal surface. These are then attributed to singly attached bacteria or to larger clusters, which are the most dangerous ones, as they could overlay corrosion pits. After that, the degradation of the material is evaluated through the quantification of microbial attachment on the surface and through dimensional distribution of bacteria clusters.