Kochetov Bogdan, Uttam Shikhar
Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.
UPMC Hillman Cancer Center, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
bioRxiv. 2024 Sep 26:2024.09.25.614955. doi: 10.1101/2024.09.25.614955.
We present an easy-to-use, nonlinear filter for effective background identification in fluorescence microscopy images with dense and low-contrast foreground. The pixel-wise filtering is based on comparison of the pixel intensity with the mean intensity of pixels in its local neighborhood. The pixel is given a background or foreground label depending on whether its intensity is less than or greater than the mean respectively. Multiple labels are generated for the same pixel by computing mean expression values by varying neighborhood size. These labels are accumulated to decide the final pixel label. We demonstrate that the performance of our filter favorably compares with state-of-the-art image processing, machine learning, and deep learning methods. We present three use cases that demonstrate its effectiveness, and also show how it can be used in multiplexed fluorescence imaging contexts and as a denoising step in image segmentation. A fast implementation of the filter is available in Python 3 on GitHub.
我们提出了一种易于使用的非线性滤波器，用于在具有密集且低对比度前景的荧光显微镜图像中有效地识别背景。逐像素滤波基于将像素强度与其局部邻域中像素的平均强度进行比较。根据像素强度分别小于或大于平均值，为该像素赋予背景或前景标签。通过改变邻域大小计算平均表达值，为同一像素生成多个标签。这些标签被累加起来以确定最终的像素标签。我们证明，我们的滤波器性能与最先进的图像处理、机器学习和深度学习方法相比具有优势。我们展示了三个用例，证明了其有效性，还展示了它如何用于多重荧光成像环境以及作为图像分割中的去噪步骤。该滤波器的快速实现可在GitHub上的Python 3中获取。
