{"title":"Cell counting based on local intensity maxima grouping for in-situ microscopy","authors":"L. Rojas, G. Martinez, T. Scheper","doi":"10.1109/ISBI.2014.6868126","DOIUrl":null,"url":null,"abstract":"In this contribution, a new algorithm to estimate the cell count from an intensity image of Baby Hamster Kidney (BHK) cells captured by an in-situ microscope is proposed. Given that the local intensity maxima inside a cell share similar location and intensity values, it is proposed to find all the intensity maxima inside each cell cluster present in the image, and then group those who share similar location and intensity values. The total number of cells present in an image is estimated as the sum of the number of groups found in each cluster. The experimental results show that the average cell count improved by 79%, and that the average image processing time improved by 42%.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISBI.2014.6868126","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
In this contribution, a new algorithm to estimate the cell count from an intensity image of Baby Hamster Kidney (BHK) cells captured by an in-situ microscope is proposed. Given that the local intensity maxima inside a cell share similar location and intensity values, it is proposed to find all the intensity maxima inside each cell cluster present in the image, and then group those who share similar location and intensity values. The total number of cells present in an image is estimated as the sum of the number of groups found in each cluster. The experimental results show that the average cell count improved by 79%, and that the average image processing time improved by 42%.
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