scikit-image: Image processing in Python

Walt, Stéfan van der; Schönberger, Johannes L.; Nunez-Iglesias, Juan; Boulogne, François; Warner, Joshua D.; Yager, Neil; Gouillart, Emmanuelle; Yu, Tony

doi:10.7287/peerj.preprints.336v2

Cited by 569 publications

(648 citation statements)

References 9 publications

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“…Matplotlib is used for graph production. Numpy, Scipy, and Scikits-Image are used for computation and analysis (van der Walt et al 2011(van der Walt et al , 2014. Experiments are initiated from a web-interface, 7 min being the minimum time interval between scans (20 min as default) and 96, 384, or 1536 pinned plates being the allowed formats.…”

Section: Physical Arrangement Of Scan-o-maticmentioning

confidence: 99%

Scan-o-matic: high-resolution microbial phenomics at a massive scale

Zackrisson

Hallin²,

Ottosson

et al. 2015

Preprint

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The capacity to map traits over large cohorts of individuals-phenomics-lags far behind the explosive development in genomics. For microbes, the estimation of growth is the key phenotype because of its link to fitness. We introduce an automated microbial phenomics framework that delivers accurate, precise, and highly resolved growth phenotypes at an unprecedented scale. Advancements were achieved through the introduction of transmissive scanning hardware and software technology, frequent acquisition of exact colony population size measurements, extraction of population growth rates from growth curves, and removal of spatial bias by reference-surface normalization. Our prototype arrangement automatically records and analyzes close to 100,000 growth curves in parallel. We demonstrate the power of the approach by extending and nuancing the known salt-defense biology in baker's yeast. The introduced framework represents a major advance in microbial phenomics by providing high-quality data for extensive cohorts of individuals and generating well-populated and standardized phenomics databases KEYWORDS

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Section: Physical Arrangement Of Scan-o-maticmentioning

confidence: 99%

Scan-o-matic: high-resolution microbial phenomics at a massive scale

Zackrisson

Hallin²,

Ottosson

et al. 2015

Preprint

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“…Reflectance data was calculated from sensor radiance using the ATCOR 2/3 software package [33]. After that, the mean structural similarity index measure (MSSIM) from Wang et al [34], implemented in scikit-image [35], was calculated for each corresponding TOAREF/reflectance pair, as shown in Table 1, according to the processing scheme and equations in in the Supplementary Materials. Table 1 clearly shows that, despite ALI, due to the small scene size, the deviations (1-MSSI) are mainly >60%.…”

Section: Preprocessing Of Multispectral Spaceborne Datamentioning

confidence: 99%

“…The mean structural similarity index measure (MSSIM) from Wang et al [34], implemented in scikit-image [35], was calculated between each generically resampled multispectral sensor image and the generically resampled HySpex reference. The MSSIM was selected as a quality indicator, because it represents a sensitive image-based measure to detect subtle changes between similar images, such as image noise, contrast and brightness changes, which can be induced by spectral resampling and interpolation.…”

Section: Linking Multispectral and Hyperspectral Data Through Laboratmentioning

confidence: 99%

Spaceborne Mine Waste Mineralogy Monitoring in South Africa, Applications for Modern Push-Broom Missions: Hyperion/OLI and EnMAP/Sentinel-2

et al. 2014

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Abstract:Remote sensing analysis is a crucial tool for monitoring the extent of mine waste surfaces and their mineralogy in countries with a long mining history, such as South Africa, where gold and platinum have been produced for over 90 years. These mine waste sites have the potential to contain problematic trace element species (e.g., U, Pb, Cr). In our research, we aim to combine the mapping and monitoring capacities of multispectral and hyperspectral spaceborne sensors. This is done to assess the potential of existing multispectral and hyperspectral spaceborne sensors (OLI and Hyperion) and future missions, such as Sentinel-2 and EnMAP (Environmental Mapping and Analysis Program), for mapping the spatial extent of these mine waste surfaces. For this task we propose a new index, termed the iron feature depth (IFD), derived from Landsat-8 OLI data to map the 900-nm absorption feature as a potential proxy for monitoring the spatial extent of mine waste. OLI was chosen, because it represents the most suitable sensor to map the IFD over large areas in a multi-temporal manner due to its spectral band layout; its (183 km × 170 km) scene size and its revisiting time of 16 days. The IFD is in good agreement with primary OPEN ACCESS Remote Sens. 2014, 6 6791 and secondary iron-bearing minerals mapped by the Material Identification and Characterization Algorithm (MICA) from EO-1 Hyperion data and illustrates that a combination of hyperspectral data (EnMAP) for mineral identification with multispectral data (Sentinel-2) for repetitive area-wide mapping and monitoring of the IFD as mine waste proxy is a promising application for future spaceborne sensors. A maximum, absolute model error is used to assess the ability of existing and future multispectral sensors to characterize mine waste via its 900-nm iron absorption feature. The following sensor-signal similarity ranking can be established for spectra from gold mining material: EnMAP 100% similarity to the reference, ALI 97.5%, Sentinel-2 97%, OLI and ASTER 95% and ETM+ 91% similarity.

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“…Two sets of 3D images were captured, one with deep illumination acquired at an incident angle closer to the critical angle, and other in the TIRF mode. The image processing is handled with scikit-image [27] library in Python. Here we present few additional experimental images to show the AxSET reconstruction.…”

Section: Supplementary Info B: Visualization Of 3d Axsetmentioning

confidence: 99%

Axial super-resolution evanescent wave tomography

et al. 2016

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Optical tomographic reconstruction of a 3D nanoscale specimen is hindered by the axial diffraction limit, which is 2-3 times worse than the focal plane resolution. We propose and experimentally demonstrate an axial super-resolution evanescent wave tomography (AxSET) method that enables the use of regular evanescent wave microscopes like Total Internal Reflection Fluorescence Microscope (TIRF) beyond surface imaging, and achieve tomographic reconstruction with axial superresolution. Our proposed method based on Fourier reconstruction achieves axial super-resolution by extracting information from multiple sets of three-dimensional fluorescence images when the sample is illuminated by an evanescent wave. We propose a procedure to extract super-resolution features from the incremental penetration of an evanescent wave and support our theory by 1D (along the optical axis) and 3D simulations. We validate our claims by experimentally demonstrating tomographic reconstruction of microtubules in HeLa cells with an axial resolution of ∼130 nm. Our method does not require any additional optical components or sample preparation. The proposed method can be combined with focal plane super-resolution techniques like STORM and can also be adapted for THz and microwave near-field tomography.Optical tomography is a major tool in threedimensional visualization of sub-micrometer scale specimens in biology, material sciences and nano-fabrication technology [1][2][3]. Tomographic reconstruction is done by optical sectioning of the object in the focal plane followed by 3D stitching of the acquired z-stack of focal plane images. Resolution of the 3D tomographic reconstruction of an object is therefore governed by the focal plane resolution and the axial resolution of the underlying optical image acquisition. A wide range of well advanced fluorescence based super-resolution microscopy techniques have been reported and are currently in practice [4]. Super-resolution [12] have increased the resolution in the axial direction with an almost spherical PSF. More recently a triple-view capture and fusion approach [13] has been reported to improve volumetric resolution by a factor of two. However, these * sarang.pendharker@ualberta.ca techniques require imaging and illuminating the same focal plane from both sides of the sample and depend on extensive optical components and precise optical phase matching. 3D STED [1] and 3D STORM [2, 3] for threedimensional localization have also been reported. Axial super-resolution can also be achieved by placing a reflective mirror behind the sample to squeeze the PSF in the axial direction by interference from the reflected STED beam [14,15]. However, STORM and STED impose constraints on properties of the fluorescent probes, limiting their applicability to image photo-switchable fluorophores and samples with a sharp emission spectrum, respectively. Evanescent wave illumination techniques like Total internal Reflection Fluorescence (TIRF) microscopy [16,17], Plasmon enhanced TIRF [18], variableangle TIRF [...

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scikit-image: Image processing in Python

Cited by 569 publications

References 9 publications

Scan-o-matic: high-resolution microbial phenomics at a massive scale

Scan-o-matic: high-resolution microbial phenomics at a massive scale

Spaceborne Mine Waste Mineralogy Monitoring in South Africa, Applications for Modern Push-Broom Missions: Hyperion/OLI and EnMAP/Sentinel-2

Axial super-resolution evanescent wave tomography

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