A fast-Fourier-transform method of topography and interferometry is proposed. By computer processing of a noncontour type of fringe pattern, automatic discrimination is achieved between elevation and depression of the object or wave-front form, which has not been possible by the fringe-contour-generation techniques. The method has advantages over moire topography and conventional fringe-contour interferometry in both accuracy and sensitivity. Unlike fringe-scanning techniques, the method is easy to apply because it uses no moving components.

Fourier-transform method of fringe-pattern analysis for computer-based topography and interferometry

Thank you for reading principles of optics electromagnetic theory of propagation interference and diffraction of light. As you may know, people have search hundreds times for their favorite novels like this principles of optics electromagnetic theory of propagation interference and diffraction of light, but end up in harmful downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they are facing with some infectious bugs inside their computer.

Principles Of Optics Electromagnetic Theory Of Propagation Interference And Diffraction Of Light

Abundant accumulation of digital histopathological images has led to the increased demand for their analysis, such as computer-aided diagnosis using machine learning techniques. However, digital pathological images and related tasks have some issues to be considered. In this mini-review, we introduce the application of digital pathological image analysis using machine learning algorithms, address some problems specific to such analysis, and propose possible solutions.

Machine Learning Methods for Histopathological Image Analysis.

Single cell analysis: the new frontier in ‘Omics’ Daojing Wang 1 and Steven Bodovitz 2 1. Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 2. BioPerspectives, San Francisco, CA Corresponding author: Wang, D. (djwang@lbl.gov) Cellular heterogeneity arising from stochastic expression of genes, proteins, and metabolites is a fundamental principle of cell biology, but single cell analysis has been beyond the capabilities of ‘Omics’ technologies. This is rapidly changing with the recent examples of single cell genomics, transcriptomics, proteomics, and metabolomics. The rate of change is expected to accelerate owing to emerging technologies that range from micro/nanofluidics to microfabricated interfaces for mass spectrometry to third- and fourth-generation automated DNA sequencers. As described in this review, single cell analysis is the new frontier in Omics, and single cell Omics has the potential to transform systems biology through new discoveries derived from cellular heterogeneity. Single cell analysis: needs and applications Cellular heterogeneity Cellular heterogeneity within an isogenic cell population is a widespread event [1, 2]. Stochastic gene and protein expression at the single cell level has been clearly demonstrated in different systems using a variety of techniques [3-5]. Therefore, analyzing cell ensembles individually with high spatiotemporal resolutions will lead to a

Single cell analysis: the new frontier in 'Omics'

two–dimensional phase unwrapping. theory, algorithms, and two dimensional phase unwrapping theory algorithms and two dimensional phase unwrapping theory algorithms and two-dimensional phase unwrapping using neural networks two-dimensional phase unwrapping: theory, algorithms, and (size 43,32mb) link download two dimensional phase phase unwrapping: project liverpool john moores university pixel-wise absolute phase unwrapping using geometric 2d phase unwrapping on fpgas and gpus phase unwrapping producing bright bands if phase unwrapping and affine transformations using cuda phase unwrapping on reconfigurable hardware ll.mit absolute three-dimensional shape measurement using coded fast twodimensional simultaneous phase unwrapping and low unwrapping differential x-ray phase-contrast images connections between transport of intensity equation and space geodesy seminar sio 239 scripps institution of experiment of phase unwrapping algorithm in interferometric reference documents esa 3d shape measurement technique for multiple rapidly moving phase unwrapping for large sar interferograms: statistical superfast phaseshifting method for 3-d shape measurement space geodesy seminar sio 239 scripps institution of off-axis quantitative phase imaging processing using cuda angular phase unwrapping of optically thick objects with a a comparison of phase unwrapping techniques in synthetic noise robust linear dynamic system for phase unwrapping fast phase processing in off-axis holography by cuda cat d2 dozer manual fiores fourier analysis of rgb fringe-projection profilometry and dynamic quantitative phase imaging for biological objects twowavelength quantitative phase unwrapping of dynamic comparison of phase unwrapping algorithms applied to feminist theory crime and social justice theoretical conscience volume 4 dr-caloriez henry and the paper route cafebr chapter 3 what is money mishkin cafebr

Two Dimensional Phase Unwrapping Theory Algorithms And Software

Recent advances in quantitative phase imaging (QPI) and artificial intelligence (AI) have opened up the possibility of an exciting frontier. The fast and label-free nature of QPI enables the rapid generation of large-scale and uniform-quality imaging data in two, three, and four dimensions. Subsequently, the AI-assisted interrogation of QPI data using data-driven machine learning techniques results in a variety of biomedical applications. Also, machine learning enhances QPI itself. Herein, we review the synergy between QPI and machine learning with a particular focus on deep learning. Furthermore, we provide practical guidelines and perspectives for further development.

Quantitative Phase Imaging and Artificial Intelligence: A Review

Performance of data-driven network for tumor classification varies with stain-style of histopathological images. This article proposes the stain-style transfer (SST) model based on conditional generative adversarial networks (GANs) which is to learn not only the certain color distribution but also the corresponding histopathological pattern. Our model considers feature-preserving loss in addition to well-known GAN loss. Consequently our model does not only transfers initial stain-styles to the desired one but also prevent the degradation of tumor classifier on transferred images. The model is examined using the CAMELYON16 dataset.

Neural Stain-Style Transfer Learning using GAN for Histopathological Images

Learning-based screening of hematologic disorders using quantitative phase imaging of individual red blood cells.

We present a deep neural network to reduce coherent noise in three-dimensional quantitative phase imaging. Inspired by the cycle generative adversarial network, the denoising network was trained to learn a transform between two image domains: clean and noisy refractive index tomograms. The unique feature of this network, distinct from previous machine learning approaches employed in the optical imaging problem, is that it uses unpaired images. The learned network quantitatively demonstrated its performance and generalization capability through denoising experiments of various samples. We concluded by applying our technique to reduce the temporally changing noise emerging from focal drift in time-lapse imaging of biological cells. This reduction cannot be performed using other optical methods for denoising.

Cycle-consistent deep learning approach to coherent noise reduction in optical diffraction tomography.

The immunological synapse (IS) is a cell-cell junction between a T cell and a professional antigen-presenting cell. Since the IS formation is a critical step for the initiation of an antigen-specific immune response, various live-cell imaging techniques, most of which rely on fluorescence microscopy, have been used to study the dynamics of IS. However, the inherent limitations associated with the fluorescence-based imaging, such as photo-bleaching and photo-toxicity, prevent the long-term assessment of dynamic changes of IS with high frequency. Here, we propose and experimentally validate a label-free, volumetric, and automated assessment method for IS dynamics using a combinational approach of optical diffraction tomography and deep learning-based segmentation. The proposed method enables an automatic and quantitative spatiotemporal analysis of IS kinetics of morphological and biochemical parameters associated with IS dynamics, providing a new option for immunological research.

Hyun-Seok Min

Papers

Quantitative Phase Imaging and Artificial Intelligence: A Review

Neural Stain-Style Transfer Learning using GAN for Histopathological Images

Learning-based screening of hematologic disorders using quantitative phase imaging of individual red blood cells.

Cycle-consistent deep learning approach to coherent noise reduction in optical diffraction tomography.

Deep-learning based three-dimensional label-free tracking and analysis of immunological synapses of CAR-T cells