Cell segmentation in fluorescence microscopy images based on multi-scale histogram thresholding

Yating Fang; Baojiang Zhong; Yating Fang; Baojiang Zhong

doi:10.3934/mbe.2023726

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 9: 16259-16278. doi: 10.3934/mbe.2023726

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Research article

Cell segmentation in fluorescence microscopy images based on multi-scale histogram thresholding

Yating Fang ,
Baojiang Zhong ^,

School of Computer Science and Technology, Soochow University, Suzhou 215021, China

Academic Editor: Vladimir Mityushev

Received: 18 May 2023 Revised: 07 August 2023 Accepted: 08 August 2023 Published: 14 August 2023

Cell segmentation from fluorescent microscopy images plays an important role in various applications, such as disease mechanism assessment and drug discovery research. Exiting segmentation methods often adopt image binarization as the first step, through which the foreground cell is separated from the background so that the subsequent processing steps can be greatly facilitated. To pursue this goal, a histogram thresholding can be performed on the input image, which first applies a Gaussian smoothing to suppress the jaggedness of the histogram curve and then exploits Rosin's method to determine a threshold for conducting image binarization. However, an inappropriate amount of smoothing could lead to the inaccurate segmentation of cells. To address this crucial problem, a multi-scale histogram thresholding (MHT) technique is proposed in the present paper, where the scale refers to the standard deviation of the Gaussian that determines the amount of smoothing. To be specific, the image histogram is smoothed at three chosen scales first, and then the smoothed histogram curves are fused to conduct image binarization via thresholding. To further improve the segmentation accuracy and overcome the difficulty of extracting overlapping cells, our proposed MHT technique is incorporated into a multi-scale cell segmentation framework, in which a region-based ellipse fitting technique is adopted to identify overlapping cells. Extensive experimental results obtained on benchmark datasets show that the new method can deliver superior performance compared to the current state-of-the-arts.
- cell segmentation,
- multi-scale,
- histogram thresholding,
- ellipse fitting,
- fluorescence microscopy images
Citation: Yating Fang, Baojiang Zhong. Cell segmentation in fluorescence microscopy images based on multi-scale histogram thresholding[J]. Mathematical Biosciences and Engineering, 2023, 20(9): 16259-16278. doi: 10.3934/mbe.2023726

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Abstract

Cell segmentation from fluorescent microscopy images plays an important role in various applications, such as disease mechanism assessment and drug discovery research. Exiting segmentation methods often adopt image binarization as the first step, through which the foreground cell is separated from the background so that the subsequent processing steps can be greatly facilitated. To pursue this goal, a histogram thresholding can be performed on the input image, which first applies a Gaussian smoothing to suppress the jaggedness of the histogram curve and then exploits Rosin's method to determine a threshold for conducting image binarization. However, an inappropriate amount of smoothing could lead to the inaccurate segmentation of cells. To address this crucial problem, a multi-scale histogram thresholding (MHT) technique is proposed in the present paper, where the scale refers to the standard deviation of the Gaussian that determines the amount of smoothing. To be specific, the image histogram is smoothed at three chosen scales first, and then the smoothed histogram curves are fused to conduct image binarization via thresholding. To further improve the segmentation accuracy and overcome the difficulty of extracting overlapping cells, our proposed MHT technique is incorporated into a multi-scale cell segmentation framework, in which a region-based ellipse fitting technique is adopted to identify overlapping cells. Extensive experimental results obtained on benchmark datasets show that the new method can deliver superior performance compared to the current state-of-the-arts.

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