Cell counting based on local intensity maxima grouping for in-situ microscopy

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  • Universidad de Costa Rica
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Details

Original languageEnglish
Title of host publication2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1344-1347
Number of pages4
ISBN (electronic)9781467319591
Publication statusPublished - 31 Jul 2014
Event2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014 - Beijing, China
Duration: 29 Apr 20142 May 2014

Publication series

Name Proceedings (International Symposium on Biomedical Imaging)
ISSN (Print)1945-7928

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%.

Keywords

    Biomedical imaging, Cell cluster segmentation, Cell counting, Cell image analysis, Detection, Grouping, In-situ microscopy, Local intensity maxima

ASJC Scopus subject areas

Cite this

Cell counting based on local intensity maxima grouping for in-situ microscopy. / Rojas, L. D.; Martinez, G.; Scheper, T.
2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 1344-1347 ( Proceedings (International Symposium on Biomedical Imaging)).

Research output: Chapter in book/report/conference proceedingConference contributionResearchpeer review

Rojas, LD, Martinez, G & Scheper, T 2014, Cell counting based on local intensity maxima grouping for in-situ microscopy. in 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014. Proceedings (International Symposium on Biomedical Imaging), Institute of Electrical and Electronics Engineers Inc., pp. 1344-1347, 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014, Beijing, China, 29 Apr 2014. https://doi.org/10.1109/isbi.2014.6868126
Rojas, L. D., Martinez, G., & Scheper, T. (2014). Cell counting based on local intensity maxima grouping for in-situ microscopy. In 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014 (pp. 1344-1347). ( Proceedings (International Symposium on Biomedical Imaging)). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/isbi.2014.6868126
Rojas LD, Martinez G, Scheper T. Cell counting based on local intensity maxima grouping for in-situ microscopy. In 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014. Institute of Electrical and Electronics Engineers Inc. 2014. p. 1344-1347. ( Proceedings (International Symposium on Biomedical Imaging)). doi: 10.1109/isbi.2014.6868126
Rojas, L. D. ; Martinez, G. ; Scheper, T. / Cell counting based on local intensity maxima grouping for in-situ microscopy. 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 1344-1347 ( Proceedings (International Symposium on Biomedical Imaging)).
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N2 - 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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