Fast Background Removal in 3D Fluorescence Microscopy Images Using One-Class Learning

With the recent advances of optical tissue clearing technology, current imaging modalities are able to image large tissue samples in 3D with single-cell resolution. However, the severe background noise remains a significant obstacle to the extraction of quantitative information from these high-resolution 3D images. Additionally, due to the potentially large sizes of 3D image data (over 10

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voxels), the processing speed is becoming a major bottleneck that limits the applicability of many known background correction methods. In this paper, we present a fast background removal algorithm for large volume 3D fluorescence microscopy images. By incorporating unsupervised one-class learning into the percentile filtering approach, our algorithm is able to precisely and efficiently remove background noise even when the sizes and appearances of foreground objects vary greatly. Extensive experiments on real 3D datasets show our method has superior performance and efficiency comparing with the current state-of-the-art background correction method and the rolling ball algorithm in ImageJ.