Artificial Intelligence and Machine Learning in Optical Information Processing: Introduction to the Feature Issue

This special feature issue covers the intersection of topical areas in artificial intelligence (AI)/machine learning (ML) and optics. The papers broadly span the current state-of-the-art advances in areas including image recognition, signal and image processing, machine inspection/vision and automotive as well as areas of traditional optical sensing, interferometry and imaging

Computational hyperspectral interferometry for studies of brain function: Proof of concept

Hyperspectral interferometric microscopy uses a unique combination of optics and algorithm design to extract information. Local brain activity rapidly changes local blood flow and red blood cell concentration (absorption) and oxygenation (color). We demonstrate that brain activity evoked during whisker stimulation can be detected with hyperspectral interferometric microscopy to identify the active whisker-barrel cortex in the rat brain. Information about constituent components is extracted across the entire spectral band. Algorithms can be flexibly optimized to discover, detect, quantify, and visualize a wide range of significant biological events, including changes relevant to the diagnosis and treatment of disease. © 2006 Optical Society of America

Simulating structured-illumination microscopy in the presence of spherical aberrations

The effect of depth-induced spherical aberrations (SA) on structured illumination microscopy (SIM)1 is investigated. SIM is a technique used in three-dimensional (3D) fluorescence microscopy to improve resolution in optical sections acquired from 3D specimens. A 3D depth-variant imaging model was developed to predict the intermediate SIM or grid images that are used by the SIM approach to compute improved optical sections. The model incorporates SA due to imaging depth within a sample when there is a refractive index (RI) mismatch between the average RI of the specimen and the RI of the immersion medium of the lens. The model was implemented using a stratum-based model approximation and multiple depth-variant point-spread functions (PSFs) 2. SIM optical sections were computed using the subtraction algorithm 1,3 and simulated grid images that include SA predicted by our model. Simulations were performed for different imaging conditions by varying the grid frequency, the amount of SA and the level of noise added to the grid images. Simulated results demonstrate that SIM images are less accurate in the presence of SA, and confirm that the SIM approach is very sensitive to system noise resulting in a reduced SNR in the optically sectioned images. © 2011 SPIE

Rotational-diversity phase estimation from differentialinterference-contrast microscopy images

An iterative phase-estimation method for the calculation of a specimen’s phase function or optical-path-length (OPL) distribution from differential-interference-contrast (DIC) microscopy images is presented. The method minimizes the least-squares discrepancy measure by use of the conjugate-gradient technique to estimate the phase function from multiple DIC images acquired at different specimen rotations. The estimate is regularized with a quadratic smoothness penalty. Results from testing the method with simulations and measured DIC images show improvement in the estimated phase when at least two rotationally diverse DIC images instead of a single DIC image are used for the estimation. The OPL of a cell that is estimated from two DIC images was found to be much more reliable than the OPL computed from single DIC images (which had a coefficient of variation equal to 15.8%). © 2000 Optical Society of America

3D reconstruction of fluorescence microscopy image intensities using multiple depth-variant point-spread functions

We show that the use of multiple depth-variant point-spread functions in 3D fluorescence intensity reconstruction provides improved optical sectioning over deconvolution methods by correcting depth-induced aberrations in 3D cell images. © 2010 Optical Society of America

Fluorescence microscopy point spread function model accounting for aberrations due to refractive index variability within a specimen

A three-dimensional (3-D) point spread function (PSF) model for wide-field fluorescence microscopy, suitable for imaging samples with variable refractive index (RI) in multilayered media, is presented. This PSF model is a key component for accurate 3-D image restoration of thick biological samples, such as lung tissue. Microscope- and specimen-derived parameters are combined with a rigorous vectorial formulation to obtain a new PSF model that accounts for additional aberrations due to specimen RI variability. Experimental evaluation and verification of the PSF model was accomplished using images from 175-nm fluorescent beads in a controlled test sample. Fundamental experimental validation of the advantage of using improved PSFs in depth-variant restoration was accomplished by restoring experimental data from beads (6  μm in diameter) mounted in a sample with RI variation. In the investigated study, improvement in restoration accuracy in the range of 18 to 35% was observed when PSFs from the proposed model were used over restoration using PSFs from an existing model. The new PSF model was further validated by showing that its prediction compares to an experimental PSF (determined from 175-nm beads located below a thick rat lung slice) with a 42% improved accuracy over the current PSF model prediction

Point-spread function engineering to reduce the impact of spherical aberration on 3D computational fluorescence microscopy imaging

Wavefront encoding (WFE) with different cubic phase mask designs was investigated in engineering 3D point-spread functions (PSF) to reduce their sensitivity to depth-induced spherical aberration (SA) which affects computational complexity in 3D microscopy imaging. The sensitivity of WFE-PSFs to defocus and to SA was evaluated as a function of phase mask parameters using mean-square-error metrics to facilitate the selection of mask designs for extended-depth-of-field (EDOF) microscopy and for computational optical sectioning microscopy (COSM). Further studies on pupil phase contribution and simulated WFE-microscope images evaluated the engineered PSFs and demonstrated SA insensitivity over sample depths of 30 μm. Despite its low sensitivity to SA, the successful WFE design for COSM maintains a high sensitivity to defocus as it is desired for optical sectioning. © 2011 Optical Society of America

Performance evaluation of an image estimation method based on principal component analysis (PCA) developed for quantitative depth-variant fluorescence microscopy imaging

In 3D wide-field computational microscopy, the image formation process is depth variant due to the refractive index mismatch between the imaging layers. In a previous study, an image estimation method based on a principle component analysis (PCA) model for the representation of the depth varying point spread function (DV-PSF) was presented and demonstrated with noiseless simulations. In this study, the performance of the PCA-based DV expectation maximization algorithm (PCA-DVEM) was further evaluated with noisy simulations. Different levels of Poisson noise were used in simulated forward images of a synthetic object computed using theoretically-determined DV-PSFs approximated by the PCA approach. The noise influence on the reconstructed images obtained with PCA-DVEM was evaluated. We found that without regularization, the algorithm performs well when the signal-to-noise ratio (SNR) is 14 dB or higher. The relationship of the number of PCA components, B, to the image reconstruction performance was also investigated on both noiseless and noisy simulated data. In both cases, we found that the number of PCA components has limited effect on the image reconstruction performance for B \u3e 1. To reduce computational complexity while maintaining image estimation performance, B = 2 is suggested for processing experimental data. © 2012 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE)

Image restoration for three-dimensional fluorescence microscopy using an orthonormal basis for efficient representation of depthvariant point-spread functions

A depth-variant (DV) image restoration algorithm for wide field fluorescence microscopy, using an orthonormal basis decomposition of DV point-spread functions (PSFs), is investigated in this study. The efficient PSF representation is based on a previously developed principal component analysis (PCA), which is computationally intensive. We present an approach developed to reduce the number of DV PSFs required for the PCA computation, thereby making the PCA-based approach computationally tractable for thick samples. Restoration results from both synthetic and experimental images show consistency and that the proposed algorithm addresses efficiently depth-induced aberration using a small number of principal components. Comparison of the PCA-based algorithm with a previously-developed strata-based DV restoration algorithm demonstrates that the proposed method improves performance by 50% in terms of accuracy and simultaneously reduces the processing time by 64% using comparable computational resources