Abstract
Modern microscopes create a data deluge with gigabytes of data generated each second, and terabytes per day. Storing and processing this data is a severe bottleneck, not fully alleviated by data compression. We argue that this is because images are processed as grids of pixels. To address this, we propose a content-adaptive representation of fluorescence microscopy images, the Adaptive Particle Representation (APR). The APR replaces pixels with particles positioned according to image content. The APR overcomes storage bottlenecks, as data compression does, but additionally overcomes memory and processing bottlenecks. Using noisy 3D images, we show that the APR adaptively represents the content of an image while maintaining image quality and that it enables orders of magnitude benefits across a range of image processing tasks. The APR provides a simple and efficient content-aware representation of fluosrescence microscopy images.
Highlights
Modern microscopes create a data deluge with gigabytes of data generated each second, and terabytes per day
If pixel resolution and image size are increased proportionally, the Adaptive Particle Representation (APR) approaches a constant number of particles (Supplementary Fig. 31). These results show that the APR adapts proportionally to image content, independent of the number of pixels, fulfilling RC2
We have introduced a content-adaptive image representation for fluorescence microscopy, the APR
Summary
Modern microscopes create a data deluge with gigabytes of data generated each second, and terabytes per day. Fluorescence microscopes do not directly output the shapes and locations of objects through time Instead, they produce raw data, potentially terabytes of 3D images[7], from which the desired spatiotemporal information must be extracted by image processing. The human visual system achieves this by adaptively sampling the scene depending on its content[10], while adjusting to the dynamic range of intensity variations[11] This adaptive sampling works by selectively focusing the attention of the eyes on areas with potentially high information content[10]. The human visual system maintains effective adaptive sampling across up to nine orders of magnitude of brightness[11] by using local gain control mechanisms that adjust to, and account for, changes in the dynamic range of intensity variations. There is a rich history of multi-resolution and adaptive sampling approaches to image processing, including super-pixels[13,14], wavelet decompositions[15,16,17], scale-space and pyramid representations[18,19], contrast-invariant level-set representations[20], dictionary-based sparse representations[21], adaptive mesh representations[22,23,24], and dimensionality reduction[25,26]
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