Adaptative signal-preserving compression of microscopic images using noise modeling in the wavelet domain and JPEG 2000 coding

Tytus Bernas; Elikplimi K. Asem; J. Paul Robinson; Bartek Rajwa

doi:10.1117/12.647701

21 February 2006 Adaptative signal-preserving compression of microscopic images using noise modeling in the wavelet domain and JPEG2000 coding

Tytus Bernas, Elikplimi K. Asem, J. Paul Robinson, Bartek Rajwa

Author Affiliations +

Proceedings Volume 6088, Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues IV; 60880Q (2006) https://doi.org/10.1117/12.647701
Event: SPIE BiOS, 2006, San Jose, California, United States

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.

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Tytus Bernas, Elikplimi K. Asem, J. Paul Robinson, and Bartek Rajwa "Adaptative signal-preserving compression of microscopic images using noise modeling in the wavelet domain and JPEG2000 coding", Proc. SPIE 6088, Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues IV, 60880Q (21 February 2006); https://doi.org/10.1117/12.647701

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KEYWORDS

Image compression

JPEG2000

Denoising

Wavelets

Microscopes

Signal to noise ratio

Luminescence

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