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Cell segmentation in time-lapse phase contrast data

Cell segmentation in time-lapse phase contrast data
Cell segmentation in time-lapse phase contrast data

The quantitative analysis of cellular migration has found many clinical applications as it can be used in the study of a large spectrum of biological processes such as tumor development and wound healing. These studies are commonly conducted on datasets that consists of a large number of timelapse images, a fact that rendered the application of human assisted procedures as unfeasible, especially when applied to large datasets. In the development of automatic tracking strategies the problem of robust cell segmentation plays a central role as the segmentation errors have adverse effects on the performance of the overall tracking process. While the phase contrast image data is often characterized by low contrast, changes in the morphology of the cells over time and cell agglomeration, the cell segmentation process is far from a trivial task. In this paper we present a new cell segmentation approach that maximizes the information related to the local contrast between the cells and the background in each image of the dataset. The proposed method has been evaluated on MDCK and HUVEC cellular datasets and experimental results are reported.

Cell segmentation, Image enhancement, Otsu thresholding, Phase contrast images
109-110
Thirusittampalam, Ketheesan
a74c8159-c033-4c37-8569-41bd37096e50
Hossain, M. Julius
bba1b875-7604-462b-a55b-ba0b54f728e8
Ghita, Ovidiu
48aa245e-63b2-445d-bda0-d1cfeca9d7ab
Whelan, Paul F.
8f491dab-3987-4dcb-886f-343fd227fc3c
Thirusittampalam, Ketheesan
a74c8159-c033-4c37-8569-41bd37096e50
Hossain, M. Julius
bba1b875-7604-462b-a55b-ba0b54f728e8
Ghita, Ovidiu
48aa245e-63b2-445d-bda0-d1cfeca9d7ab
Whelan, Paul F.
8f491dab-3987-4dcb-886f-343fd227fc3c

Thirusittampalam, Ketheesan, Hossain, M. Julius, Ghita, Ovidiu and Whelan, Paul F. (2011) Cell segmentation in time-lapse phase contrast data. In Proceedings - 2011 Irish Machine Vision and Image Processing Conference, IMVIP 2011. pp. 109-110 . (doi:10.1109/IMVIP.2011.30).

Record type: Conference or Workshop Item (Paper)

Abstract

The quantitative analysis of cellular migration has found many clinical applications as it can be used in the study of a large spectrum of biological processes such as tumor development and wound healing. These studies are commonly conducted on datasets that consists of a large number of timelapse images, a fact that rendered the application of human assisted procedures as unfeasible, especially when applied to large datasets. In the development of automatic tracking strategies the problem of robust cell segmentation plays a central role as the segmentation errors have adverse effects on the performance of the overall tracking process. While the phase contrast image data is often characterized by low contrast, changes in the morphology of the cells over time and cell agglomeration, the cell segmentation process is far from a trivial task. In this paper we present a new cell segmentation approach that maximizes the information related to the local contrast between the cells and the background in each image of the dataset. The proposed method has been evaluated on MDCK and HUVEC cellular datasets and experimental results are reported.

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More information

Published date: September 2011
Venue - Dates: 2011 15th Irish Machine Vision and Image Processing Conference, IMVIP 2011, , Dublin, Ireland, 2011-09-08 - 2011-09-09
Keywords: Cell segmentation, Image enhancement, Otsu thresholding, Phase contrast images

Identifiers

Local EPrints ID: 467289
URI: http://eprints.soton.ac.uk/id/eprint/467289
PURE UUID: 0b2014ee-f736-4433-85ee-3d051aa585fb
ORCID for M. Julius Hossain: ORCID iD orcid.org/0000-0003-3303-5755

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Date deposited: 05 Jul 2022 16:44
Last modified: 17 Mar 2024 04:12

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Contributors

Author: Ketheesan Thirusittampalam
Author: M. Julius Hossain ORCID iD
Author: Ovidiu Ghita
Author: Paul F. Whelan

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