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VISTA Lab
We use modern computer graphics tools such as ray-tracing, digital camera simulation tools, and a physically accurate model of seawater constituents to simulate how light is captured by the imaging sensor in a digital camera placed in underwater ocean environments. Our water model includes parameters for the type and amount of phytoplankton, the concentration of chlorophyll, the amount of dissolved organic matter (CDOM) and the concentration of detritus (non-algal particles, NAP). We show that by adjusting the model parameters, we can predict real sensor data captured by a digital camera at a fixed distance and depth from a reference target with known spectral reflectance. We also provide an open-source implementation of all our tools and simulations. This repository contains sample data (camera images and simulation results) that demonstrates how the simulation environment works and that allows to reproduce the analyses we present in publications.
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VISTA Lab
Illumination plays an important role in the image capture process. Too little or too much energy in particular wavelengths can impact the scene appearance in a way that is difficult to manage by color constancy post processing methods. We use an adjustable multispectral flash to modify the spectral illumination of a scene. The flash is composed of a small number of narrowband lights, and the imaging system takes a sequence of images of the scene under each of those lights. Pixel data is used to estimate the spectral power distribution of the ambient light, and to adjust the flash spectrum either to match or to complement the ambient illuminant. The optimized flash spectrum can be used in subsequent captures, or a synthetic image can be computationally rendered from the available data. Under extreme illumination conditions images captured with the matching flash have no color cast, and the complementary flash produces more balanced colors. The proposed system also improves the quality of images captured in underwater environments. This repository contains sample data and results to be used with the released Matlab implementation of the algorithms.
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VISTA Lab
There are many scientific, medical and industrial imaging applications where users have full control of the scene illumination and color reproduction is not the primary objective. For example, it is possible to co-design sensors and spectral illumination in order to classify and detect changes in biological tissues, organic and inorganic materials, and object surface properties. In this paper, we propose two different approaches to illuminant spectrum selection for surface classification. In the first approach, a supervised framework, we formulate a biconvex optimization problem where we alternate between optimizing support vector classifier weights and optimal illuminants. In the second approach, an unsupervised dimensionality reduction, we describe and apply a new sparse Principal Component Analysis (PCA) algorithm. We efficiently solve the non-convex PCA problem using a convex relaxation and Alternating Direction Method of Multipliers (ADMM). We compare the classification accuracy of a monochrome imaging sensor with optimized illuminants to the classification accuracy of conventional RGB cameras with natural broadband illumination. This repository contains sample image data used in the analysis and classification results.
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VISTA Lab
Many creative ideas are being proposed for image sensor designs, and these may be useful in applications ranging from consumer photography to computer vision. To understand and evaluate each new design, we must create a corresponding image-processing pipeline that transforms the sensor data into a form that is appropriate for the application. The need to design and optimize these pipelines is time-consuming and costly. We explain a method that combines machine learning and image systems simulation that automates the pipeline design. The approach is based on a new way of thinking of the image-processing pipeline as a large collection of local linear filters. We illustrate how the method has been used to design pipelines for novel sensor architectures in consumer photography applications.
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VISTA Lab
There is widespread interest in estimating the fluorescence properties of natural materials in an image. However, the separation between reflected and fluoresced components is difficult, because it is impossible to distinguish reflected and fluoresced photons without controlling the illuminant spectrum. We show how to jointly estimate the reflectance and fluorescence from a single set of images acquired under multiple illuminants. We present a framework based on a linear approximation to the physical equations describing image formation in terms of surface spectral reflectance and fluorescence due to multiple fluorophores. We relax the non-convex, inverse estimation problem in order to jointly estimate the reflectance and fluorescence properties in a single optimization step and we use the Alternating Direction Method of Multipliers (ADMM) approach to efficiently find a solution. We provide a software implementation of the solver for our method and prior methods. We evaluate the accuracy and reliability of the method using both simulations and experimental data. To acquire data to test the methods, we built a custom imaging system using a monochrome camera, a filter wheel with bandpass transmissive filters and a small number of light emitting diodes. We compared the system and algorithm performance with the ground truth as well as with prior methods. Our approach produces lower errors compared to earlier algorithms. This data repository contains example raw inputs and analysis results of the algorithms performing the reflectance and fluorescence separation.
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VISTA Lab
This site houses sample data and code for the publication, Takemura H, Caiafa CF, Wandell BA, Pestilli F (2016) Ensemble Tractography. PLoS Comput Biol 12(2): e1004692. doi:10.1371/journal.pcbi.1004692 All code in this repository is written in MATLAB (Mathworks) and, together with the included data, can be used to reproduce several of the figures from the publication. Code and data are provided as part of the goal of ensuring that computational methods are reproducible by other researchers. Note: This version of the repository is still under progress, and does not include the LiFE and ET code in the newest release. We are also preparing the GitHub repository for hosting the updated version of the script for reproducing the figure on this paper. Github repository (still under private, but will be available for public upon the publication) https://github.com/vistalab/EnsembleTractography
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VISTA Lab
A sample MRI data and quantitative a running scripts and MRI quantitative map outcomes form mrQ Nov. 2015.
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VISTA Lab
This site houses sample data and code for the publication, Takemura, H., Rokem, A., Winawer, J., Yeatman, J.D., Wandell, B. A., and Pestilli, F. A major human white-matter pathway between dorsal and ventral visual cortex. All code in this repository is written in MATLAB (Mathworks) and, together with the included data, can be used to reproduce several of the figures from the publication. Code and data are provided as part of the goal of ensuring that computational methods are reproducible by other researchers.

9. LiFE Demo Data [2014] Online

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VISTA Lab
The file in this repository contains demo data for the LiFE software package: http://francopestilli.github.io/life/ The data set can be used in combination with the function life_demo.m: http://francopestilli.github.io/life/doc/scripts/life_demo.html The file contains: (1) Diffusion imaging data acquired at the Center for Neurobiological Imaging Stanford University. (2) High-resolution anatomical T1w MRI images of the same brain coregistered to the diffusion data. (3) Three connectomes generated using the same diffusion data in (1) and the tractogrpahy toolbox MRtrix (http://www.brain.org.au/software/mrtrix/). The three connectomes are created using different tractography algorithms. Two connectomes were generated using constrined-spherical deconvolution (CSD) models and either probabilistic or deterministic tractography. The third connectome was generated using a tensor model and deterministic tractography.
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VISTA Lab
This site houses sample data and code for the publication, Winawer, J., Kay, K.N., Foster, B.L., Rauschecker, A.M., Parvizi, J., and Wandell, B.A. (2013). Asynchronous broadband signals are the principal source of the BOLD response in human visual cortex. Current Biology 23(13). doi:10.1016/j.cub.2013.05.001 All code in this repository is written in MATLAB (Mathworks) and, together with the included data, can be used to reproduce several of the figures from the publication. Code and data are provided as part of the goal of ensuring that computational methods are reproducible by other researchers.
Collection
VISTA Lab
Subjects were two healthy male participants, age 37 and 27. The experimental procedures were approved by the Stanford University Institutional Review Board and participants provided informed consent. Diffusion-weighted MRI data were collected at the Center for Cognitive and Neurobiological Imaging at Stanford University on 3T GE Discovery MR750 MRI system. A 32-channel head coil was used. In each scan MR images were acquired with a dual spin echo diffusion-weighted sequence for 150 different directions of diffusion-weighting, determined by an electro-static repulsion algorithm (Jones, Horsfield, & Simmons, 1999). The spatial resolution of the measurement was 2x2x2 mm. In different scans, b-values were set to 1000, 2000 and 4000 s/mm2 and respectively, TE values were: 83.1/93.6/106.9 msec. 10 non-diffusion weighted images (b0) were acquired at the beginning of each scan. Two scans were performed in each b-value in immediate succession. Data at a b-value of 2000 were collected in one session and data at a b-value of 1000 and 4000 were collected in a separate session. Segmentation of different types of tissue was performed on high-resolution T1-weighted image. Two FSPGR images were acquired at 0.7x0.7x0.7 mm resolution and averaged to increase SNR of tissue contrast. An initial segmentation was performed using Freesurfer (Dale et al., 1999) and additional manual editing of the segmentation was then performed using itkgray (Yushkevich et al., 2006). MR images were motion corrected to the average b0 image in each scan, using a rigid body alignment algorithm, implemented in SPM (http://www.fil.ion.ucl.ac.uk/spm/). The direction of the diffusion-gradient in each diffusion-weighted volume was corrected using the rotation parameters from the motion correction procedure. Because of the relatively long duration between the RF excitation and image acquisition in the dual-spin echo sequence used, there is sufficient time for eddy currents to subside. Hence, eddy current correction was not applied. All pre-processing steps have been implemented in Matlab as part of the mrVista software distribution (Dougherty, Ben-Shachar, Bammer, Brewer, & Wandell, 2005), which can be downloaded at http://github.com/vistalab/vistasoft.
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VISTA Lab
Diffusion-weighted MRI acquisition Magnetic Resonance Imaging diffusion-weighted data (DWI) were collected at Stanford’s Center for Cognitive and Neurobiological Imaging (http://cni.stanford.edu). We collected data in five males, age 37 - 39 using a 3T General Electric Discovery 750 (General Electric Healthcare, Milwaukee, WI) equipped with a 32-channel head coil (Nova Medical, Wilmington, MA). Data collection procedures were approved by the Stanford University Institutional Review Board. For each subject we acquired two diffusion weighted scans within a single scan session. Water diffusion was measured at 96 different directions across the surface of a sphere as determined by the electro-static repulsion algorithm of Jones, Horsfield, & Simmons (1999). In all subjects, data were acquired at 1.5 mm3 spatial resolution and diffusion gradient strength was set to 2000 s/mm2 (TE 96.8 msec). We used a dual-spin echo diffusion-weighted sequences with full head coverage. Individual data sets were acquired with using two excitations (nex = 2) that were averaged in k-space. We obtained 10 non-diffusion weighted (b=0) images at the beginning of each data set. The signal-to-noise-ratio calculated over repeats of the non-diffusion images was greater than 20 in all data sets. For the subject used as example in the figures we also acquired two data sets with 150 directions at 2 mm3 spatial resolution and b values of 1000, 2000 and 4000 s/mm2 (TE 83.1/93.6/106.9 msec). MRI images were corrected for spatial distortions due to B0 field inhomogeneity. To do so we measured the B0 magnetic field maps. Field maps were collected in the same slices as the functional data using a 16-shot, gradient-echo spiral-trajectory pulse sequence. Two volumes were successively acquired one with TE set to 9.091 ms and the other with TE increased by 2.272 ms, and the phase difference between the volumes was used as an estimate of the magnetic field. To track slow drifts in the magnetic field (e.g. due to gradient heating), field maps were collected before, after and between the two diffusion scans. Subjects’ motion was corrected using a rigid body alignment algorithm (SPM). Diffusion-gradients were adjusted to account for the rotation applied to the measurements during motion correction. The dual-spin echo sequence we used does not require eddy current correction because it has a relatively long delay between the RF excitation pulse and image acquisition. This allows for sufficient time for the eddy currents to dephase. Pre-processing are publicly available as part of the vistasoft software distribution (Dougherty, Ben-Shachar, Bammer, Brewer, & Wandell, 2005; http://github.com/vistalab/vistasoft/mrDiffusion). Anatomical MRI acquisition and tissue segmentation The white- and gray-matter border was defined using a 0.7 mm3 T1-weighted FSPGR image. White/gray matter tissue contrast was increased by averaging two T1 measurements acquired in the same scan session. An initial segmentation was performed using an automated procedure (Freesurfer) and refined manually (http://www.itksnap.org/pmwiki/pmwiki.php).
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VISTA Lab
This is a sample MRI data and quantitative map outcomes of this data for the publication: Mezer A, Yeatman JD, Stikov N, Kay K, Cho NJ, Dougherty R, Perry M, Parvizi J, Hua L, Butts-Pauly K, Wandell BA. "Quantifying the local tissue volume and composition in individual brains with MRI." Nature Medicine 2013. The code for the analysis of this data can be found in: https://github.com/mezera/mrQ.
Collection
VISTA Lab
Magnetic Resonance Spectroscopy was collected at the Stanford Center for Neurobiological and Cognitive Imaging (CNI), using a stock GE MEGA PRESS sequence, at a resolution of 2.5 x 2.5 x 2.5 mm^3. Data was collected using either pure GABA (50 mM) phantom, and in one healthy individual. For this individual, T1-weighted images were collected at a resolution of 0.9 x 0.9 x 0.9 mm^3.
Collection
VISTA Lab
MRI Measurements were conducted on 6 healthy male participants (ages: 27-40), who provided informed consent, according to a protocol approved by the Stanford University Institutional Review Board. MRI data were collected at the Center for Cognitive and Neurobiological Imaging at Stanford University on 3T GE Discovery MR750 MRI system. A 32-channel head coil was used. A twice-refocused spin echo diffusion-weighted sequence was used. The spatial resolution of the measurement was 1.5x1.5x1.5 mm^3. 96 diffusion-weighting directions were used, with a b-value was used: 2000 s/mm^2 (TE=96.8 msec). 10 b=0 images were acquired at the beginning of each scan. Two repeated sets of images were acquired in immediate succession. To mitigate the effects of EPI spatial distortions, measurements of the B0 magnetic field were performed. Field maps were collected in the same slices as the DWI data using a 16 shot, gradient echo spiral trajectory pulse sequence. Two volumes were successively acquired, one with TE set to 9.091 ms and one with TE increased by 2.272 ms, and the phase difference between the volumes was used as an estimate of the magnetic field. To track slow drifts in the magnetic field (e.g., due to gradient heating) field maps were collected before and after the DWI scans and between successive DWI scans. The B0 field maps were smoothed in space and time using local linear regression and then used to guide multifrequency reconstruction of the spiral-based functional volumes and unwarping of the EPI-based functional volumes. The reconstructed MR images were motion corrected to the average b=0 image in each scan, using a rigid body alignment algorithm, implemented in SPM (http://www.fil.ion.ucl.ac.uk/spm/). The direction of the diffusion-gradient in each diffusion-weighted volume was corrected using the rotation parameters from the motion correction procedure. Because of the relatively long duration between the RF excitation and image acquisition in the twice-refocused spin echo sequence used, there is sufficient time for eddy currents to subside. Hence, eddy current correction was not applied. All pre-processing steps have been implemented in Matlab as part of the vistasoft software distribution which can be downloaded at http://github.com/vistalab/vistasoft.