Segmentation-based retrospective shading correction in fluorescence microscopy E. coli images for quantitative analysis

Fei Mai; Chunqi Chang; Wenqing Liu; Weichao Xu; Yeung Sam Hung

doi:10.1117/12.847036

30 October 2009 Segmentation-based retrospective shading correction in fluorescence microscopy E. coli images for quantitative analysis

Fei Mai, Chunqi Chang, Wenqing Liu, Weichao Xu, Yeung Sam Hung

Author Affiliations +

Proceedings Volume 7498, MIPPR 2009: Remote Sensing and GIS Data Processing and Other Applications; 74983O (2009) https://doi.org/10.1117/12.847036
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China

Abstract

Due to the inherent imperfections in the imaging process, fluorescence microscopy images often suffer from spurious intensity variations, which is usually referred to as intensity inhomogeneity, intensity non uniformity, shading or bias field. In this paper, a retrospective shading correction method for fluorescence microscopy Escherichia coli (E. Coli) images is proposed based on segmentation result. Segmentation and shading correction are coupled together, so we iteratively correct the shading effects based on segmentation result and refine the segmentation by segmenting the image after shading correction. A fluorescence microscopy E. Coli image can be segmented (based on its intensity value) into two classes: the background and the cells, where the intensity variation within each class is close to zero if there is no shading. Therefore, we make use of this characteristics to correct the shading in each iteration. Shading is mathematically modeled as a multiplicative component and an additive noise component. The additive component is removed by a denoising process, and the multiplicative component is estimated using a fast algorithm to minimize the intra-class intensity variation. We tested our method on synthetic images and real fluorescence E.coli images. It works well not only for visual inspection, but also for numerical evaluation. Our proposed method should be useful for further quantitative analysis especially for protein expression value comparison.

Citation Download Citation

Fei Mai, Chunqi Chang, Wenqing Liu, Weichao Xu, and Yeung Sam Hung "Segmentation-based retrospective shading correction in fluorescence microscopy E. coli images for quantitative analysis", Proc. SPIE 7498, MIPPR 2009: Remote Sensing and GIS Data Processing and Other Applications, 74983O (30 October 2009); https://doi.org/10.1117/12.847036

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