Improved cell segmentation with adaptive bi-Gaussian mixture models for image contrast enhancement pre-processing

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Conference Proceeding
2017 IEEE Life Sciences Conference, LSC 2017, 2018, 2018-January pp. 87 - 90
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© 2017 IEEE. The accurate detection and segmentation of cells from time-lapse microscopic video sequences provides a critical foundation for understanding dynamic cell behaviours and cell characteristics when using automatic cell tracking systems. However, general object segmentation methods in computer vision are susceptible to errors due to the severe microscopic imaging conditions in time-lapse cell videos. To address the low image intensity contrast typical in cell images, this paper investigates the use of an adaptive, shifted bi-Gaussian mixture model to enhance the contrast prior to cell segmentation. Rather than using a model with fixed parameters across an entire video sequence as in existing approaches, this paper proposes the adaptive derivation of the mixture model parameters to match the intensity histogram for each video frame to adaptively address changes in the video background. Experimental results across a cell database show improved segmentation accuracy compared with existing image contrast enhancement methods. The pre-processed cell image exhibits greater differentiation between the cell foreground and background, whilst also maintaining the original intensity histogram features.
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