Discrete Chebyshev moments (due to discrete polynomial basis) do not have the discretization errors that continuous-domain Legendre and Zernike moments contain. Calculation of polynomial basis coefficients of discrete moments is generally performed using recurrence relationships. Such recurrence equations cause numerical error accumulation especially for calculation of higher-order moments and for larger image sizes, causing significant degradation of image reconstruction from these moments. A method for better image reconstruction from high orders of discrete Chebyshev moments is demonstrated. This is accomplished by calculating Chebyshev polynomial coefficients directly from their definition formulas using arbitrary precision arithmetic and by forming lookup tables from these coefficients.
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.
Prolonged excitation of fluorescent probes leads eventually to loss of their capacity to emit light. A decrease in the number of detected photons reduces subsequently the resolving power of a fluorescence microscope. Adverse effects of fluorescence intensity loss on the quality of microscopic images of biological specimens have been recognized, but not determined quantitatively. We propose three human-independent methods of quality determination. These techniques require no reference images and are based on calculation of the actual resolution distance, information entropy, and signal-to-noise ratio (SNR). We apply the three measures to study the effect of photobleaching in cell nuclei stained with propidium iodide (PI) and chromomycin A3 (CA3) and imaged with fluorescence confocal microscopy. We conclude that the relative loss of image quality is smaller than the corresponding decrease in fluorescence intensity. Furthermore, the extent of quality loss is related to the optical properties of the imaging system and the noise characteristics of the detector. We discuss the importance of these findings for optimal registration and compression of biological images.
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 optimised to preserve visual quality but not necessarily the integrity of the scientific data. Here, we propose three observer-independent compression algorithms, designed to preserve information contained in the images. These algorithms were constructed using signal to noise ratio (SNR) computed from a single image as a quality measure to establish which image components may be discarded. Signal to noise ratio (SNR) was used in this study to construct three lossy compression techniques, which preserve information contained in the images. The compression efficiency was measured as a function of image brightness (and SNR). Furthermore, the alterations introduced by compression were estimated using brightness histograms (earth’s mover distance algorithm) and textures (Haralick parameters).
SC738: Biological Constraints of Optical Imaging
The physical limitations of biological optical microscopy are well established. However, considerably less attention is paid to the fact that the biological nature of the objects studied imposes additional constraints on microscopic imaging of cells and tissues. Biological systems are, by definition, dynamic. Therefore, any experimental procedure should address the biological and chemical changes during measurement in the studied system. The imaging itself may induce some of such changes, whereas others variations occur independently of microscopic observations.
The goal of this short course is to present the factors that limit the accuracy, resolution, and reproducibility of microscopic imaging of biological objects. The discussion will focus on two methods of 3D optical imaging: confocal microscopy and two-photon microscopy. The course will recapitulate the fundamental physical limitations of optical imaging, and reevaluate their meaning in the context of practical biological microscopy. The following subjects will be discussed: influence of photon statistics and instrumental noise on accuracy and resolution, photophysical and biochemical stability of fluorescence labels, photodamage and phototoxicity, autofluorescence, and intrinsic optical properties of biological specimens.
The course will also address the important issues of calibration and standardization. The performance of microscopic imaging of biological samples is usually evaluated in qualitative and subjective manner. There is no versatile, widely adopted standard for evaluation of optical microscopes used for biological studies, or for the quality of biological images collected. One of the aims of this short-course is to identify a set of statistical procedures for evaluation of microscope performance in the context of cell studies.