Automated cell segmentation for microscopy cell images has recently become an initial step for further image analysis in cell biology. However, microscopy cell images are easily degraded by noise during the readout procedure via optical-electronic imaging systems. Such noise degradations result in low signal-to-noise ratio (SNR) and poor image quality for cell identification. In order to improve SNR for subsequent segmentation and image-based quantitative analysis, the commonly used state-of-art restoration techniques are applied but few of them are suitable for corrupted microscopy cell images. In this paper, we propose a multistaged method based on a novel integration of trend surface analysis, quantile-quantile plot, bootstrapping, and the Gaussian spatial kernel for the restoration of noisy microscopy cell images. We show this multistaged approach achieves higher performance compared with other state-of-art restoration techniques in terms of peak signal-to-noise ratio and structure similarity in synthetic noise experiments. This paper also reports an experiment on real noisy microscopy data which demonstrated the advantages of the proposed restoration method for improving segmentation performance.
Algorithms Artifacts