Adaptative, signal-preserving compression of microscopic images using noise modeling in the wavelet domain and JPEG2000 coding

Abstract

Modern microscopic techniques, like high-content, high-throughput screening (HCS), may involve collection of thousands of images per experiment. Efficient image-compression techniques are indispensable to manage these vast amounts of data. Such compression may be obtained with lossy compression algorithms such as JPEG and JPEG2000. However, these algorithms are optimized to preserve visual quality but not necessarily the integrity of the scientific data. Here, we describe an observer-independent compression algorithm designed to preserve information contained in microscope images. We construct a model of noise as a function of signal in our imaging system, using the imaged specimen as the standard. The noise and signal are then calculated in the wavelet domain for each pixel of a single image. The SNR (signal-to-noise ratio) is used as a quality measure to establish which image components may be discarded. The denoised images, coded using reversible JPEG2000, require less storage space than their non-denoised counterparts. We used model images and microscope test patterns (grating arrays) to demonstrate that the proposed denoising scheme does not alter the effective microscope modulation transfer function (MTF) when used in conjunction with lossless JPEG2000. Furthermore, we confirm these findings by estimating the alterations introduced by compression of images of cell nuclei using brightness histograms (earth's mover distance algorithm) and several texture parameters. We demonstrate that the proposed denoising procedure reduces artifacts when used as a preprocessing step for irreversible JPEG2000 in model as well as in real biological images.