Texture analysis microscopy: quantifying structure in low-fidelity images of dense fluids Yongxiang Gao1,2 and Matthew E. Helgeson1,* 1

Department of Chemical Engineering, University of California Santa Barbara, 3357 Engineering II, Santa Barbara, CA 93106, USA Current address: Department of Chemistry, Physical and Theoretical Chemistry Laboratory, University of Oxford, South Parks Road, Oxford, OX1 3QZ, UK * [email protected]

2

Abstract: Optical images are often corrupted by noise, low contrast, uneven illumination and artefacts, which may pose significant challenges to image analysis, particularly for dense fluids. Traditionally, noise removal and contrast enhancement are achieved by global arithmetic operations on the image as a whole, and/or by image convolution with various kernels. However, these methods work under very limited conditions and can compromise detail within the image. Here, we develop a new technique, texture analysis microscopy (TAM), to overcome these challenges based on the method of image correlation. TAM recasts an image by the statistical similarities between a raw image and a template feature (e.g. a Gaussian) that best approximates features in the image. We demonstrate the superiority of TAM by applying it to low-fidelity images under conditions where traditional methods fail or have deteriorative performance, for analyses including structural correlations, particle identification and sizing. ©2014 Optical Society of America OCIS codes: (100.2960) Image analysis; (180.0180) Microscopy; (100.2980) Image enhancement; (100.4999) Pattern recognition, target tracking; (070.4790) Spectrum analysis.

References and links 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13.

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#206682 - $15.00 USD Received 18 Feb 2014; revised 25 Mar 2014; accepted 28 Mar 2014; published 18 Apr 2014 (C) 2014 OSA 21 April 2014 | Vol. 22, No. 8 | DOI:10.1364/OE.22.010046 | OPTICS EXPRESS 10046

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1. Introduction Optical images are often taken under nonideal conditions. For example, bright-field images may lack contrast when samples have poor absorption of light or there is little difference in refractive index between the structure and background [1]; multiple scattering can obscure features in opaque materials [2]; signal-to-noise ratio may be traded for speed in pursuit of

#206682 - $15.00 USD Received 18 Feb 2014; revised 25 Mar 2014; accepted 28 Mar 2014; published 18 Apr 2014 (C) 2014 OSA 21 April 2014 | Vol. 22, No. 8 | DOI:10.1364/OE.22.010046 | OPTICS EXPRESS 10047

temporal resolution or less exposure time to avoid photo bleaching [3]; significant local or global variation in signal intensity may result from variation in fluorescence-labelling or uneven illumination [4]; optical artefacts with intensity similar to particles may emerge from bright-field images; in addition, experimental systems often have polydisperse or even poorly-defined features [5,6]. Such low-fidelity images pose significant challenges to image analysis, particularly for dense fluids where image correction methods are not easily applied. Various methods have been implemented to suppress noise and enhance contrast. Simple approaches include background subtraction and rescaling the intensity to fill the entire dynamic range, averaging over several images or binning pixels to reduce noise [1]. More complex methods include convolution of images with a smoothing kernel [7,8], subtraction of local background [7] and wavelet segmentation [9]. All these methods essentially alter the pixel intensity by global arithmetic operations on the image as a whole, or by performing a weighted sum of the pixel and its surrounding ones. However, these operations may not be sufficient for low-fidelity images (Fig. 1), including structural correlation in low-resolution images [Fig. 1(a)] and images with uneven illumination [Fig. 1(b)], feature localization in images with bright optical artefacts [Fig. 1(c)], and analysis of dense, polydisperse systems [Fig. 1(d)].

Fig. 1. (a) Low-contrast bright-field image of a fluid undergoing phase separation. (b) Inverted confocal image of a layer of colloidal rods sitting on a coverslip with uneven illumination. (c) Optical image of a 2D colloidal crystal with uneven illumination and bright optical artefacts at defects. (d) Optical image of a concentrated colloidal system with binary size distribution.

In this paper, we present a new image processing technique based on image correlation, textural analysis microscopy (TAM), which provides support for low-fidelity images. Unlike traditional methods which perform arithmetic operations on the pixel intensity, TAM reconstructs an image based on the statistical similarity, quantified by the correlation coefficient, between a raw image and a template feature that is optimized so that the resulting correlation matrix best approximates basic features in the original image. Similar methods

#206682 - $15.00 USD Received 18 Feb 2014; revised 25 Mar 2014; accepted 28 Mar 2014; published 18 Apr 2014 (C) 2014 OSA 21 April 2014 | Vol. 22, No. 8 | DOI:10.1364/OE.22.010046 | OPTICS EXPRESS 10048

have been previously used to gain information about the content of an image. For example, a method based on statistical correlation of pixel intensities have been used as a metric of entropy and contrast within an image [10,11]. However, this method only gives global metrics which discard the local-scale information contained in an image. The idea of constructing correlation images has also been implemented previously for enhanced visualization of biological tissues [12], in which case the template texture is arbitrarily chosen as a small area from the original image. However, this approach is unnecessarily biased toward a desired feature size, morphology and orientation in the image (for anisotropic template textures). TAM takes a more rigorous approach by posing a canonical template texture whose morphology is first globally optimized, and then locally adjusted to features within the image. We apply TAM as an initial step preceding two popular image analysis methods. The first is the fast Fourier transform (FFT), which provides a convenient analysis of structural correlations and has been used to understand physical processes including the dynamics of phase separation [13–17]. The second is feature localization, which extracts multiple feature coordinates in high precision and is a crucial technique for understanding structural dynamics and mechanical properties of particulate-based fluids [7,18–22] and granular media [23], observing transport phenomena in biology [24,25], and super-resolution localization microscopy [26–29]. We show that TAM is efficient in smoothing noise, enhancing contrast, and removing artefacts and non-uniform intensity across the image. As a result, TAM can resolve structural correlations, feature size distributions, and feature locations with high fidelity in images where traditional methods have corrupted performance, or completely fail. 2. Texture analysis microscopy The TAM algorithm produces a series of textural correlation maps of a single real image consisting of a matrix A of grayscale pixel intensities. The principle assumption of the algorithm is that the real image contains a number of point-like features which define the topology of the texture. Furthermore, we assume that the intensity distribution for these textural features is similar to a theoretical function corresponding to a prescribed textural kernel with variable morphological parameters. Briefly, the algorithm for TAM is as follows: (1) Define a textural kernel possessing variable morphological features. (2) Using the method of image correlations, globally optimize the morphological parameters across the entire image. (3) Produce a correlation image from the most probable feature morphology. (4) Using the optimized textural kernel as an aid, identify the locations of individual textural features. (5) Locally optimize the morphological parameters to each individual feature, producing a distribution of textural features. 2.1 Textural mapping To illustrate our method, we apply texture analysis to a bright-field image of a colloidal fluid undergoing phase separation, Fig. 2(a). We begin by defining a template matrix of intensity values, G, with dimension (2w + 1) × (2w + 1), where the elements of G represent pixel intensities given by the textural kernel. Here we choose a Gaussian kernel with variance wg2 centered at Gw+1,w+1, where the Gaussian is discretized by the intensity at the center of each pixel element. Thus, G represents the intensity of a prototypical textural feature with a size represented by 2w + 1 and edge sharpness represented by wg−1. We choose a Gaussian as a useful generic feature, as it corresponds to the point spread function for a point scatterer [1]. The kernel is thus defined by the morphological features w and wg, i.e., G(w,wg). An example is shown in the inset of Fig. 2(a).

#206682 - $15.00 USD Received 18 Feb 2014; revised 25 Mar 2014; accepted 28 Mar 2014; published 18 Apr 2014 (C) 2014 OSA 21 April 2014 | Vol. 22, No. 8 | DOI:10.1364/OE.22.010046 | OPTICS EXPRESS 10049

Fig. 2. Texture analysis and optimization. (a) Representative bright-field optical image of a colloidal fluid undergoing spinodal phase separation. Inset shows a Gaussian template matrix, G(w,wg). (b) Correlation coefficient map for the original image constructed based on the template matrix in (a). (c) We identify the local maxima of correlation coefficients (blue dots) using the Crocker and Grier centroid localization algorithms. (d) Template matrixes of various sizes and edge sharpness used to construct correlation coefficient map. (e) The average values of local maxima in correlation coefficient maps constructed based on the templates in (d).

We define a region of interest (ROI) within the image by defining B(x,y,w), a sub-matrix of A also with dimension (2w + 1) × (2w + 1), centered around pixel (x,y). We then compare the statistical similarity between the ROI and the textural kernel by calculating the correlation coefficient, ρ, of B and G, defined by 2 w +1 2 w +1

ρ ( x, y , w, wg ) = corr  B ( x,y,w ) , G ( w, wg ) =

  (B i =1

j =1

i, j

− B )( Gi , j − G )

2 ( 2 w + 1) σ Bσ G

(1)

where B and G , σB and σG are the mean and standard deviation, respectively, of the respective matrices. The correlation coefficient takes values in the range [-1, 1], with 0 corresponding to minimal correlation, and 1 and −1 correspond to perfect correlation and anticorrelation, respectively. Thus, ρ(x,y,w,wg) can be related to the likelihood of finding a feature with textural properties w and wg centered at pixel (x,y). By calculating ρ(x,y,w,wg) for all pixels, a map of the correlation coefficient T(w,wg) for the original image A is generated for a particular value of w and wg, Fig. 2(b). For pixels within a distance w from the boundaries of the image, it becomes impossible to define an ROI of dimension (2w + 1) × (2w + 1) centered at these pixels, and so these pixels are disregarded. This results in a textural map 2w smaller than the origin image.

2.2 Feature recognition To locate individual features within the image matrix A, we then identify the locations of local maxima of T(w,wg) using a standard feature-finding algorithm [7,30]. The algorithm has two important input parameters, r and Icut, which set the minimum separations between two

#206682 - $15.00 USD Received 18 Feb 2014; revised 25 Mar 2014; accepted 28 Mar 2014; published 18 Apr 2014 (C) 2014 OSA 21 April 2014 | Vol. 22, No. 8 | DOI:10.1364/OE.22.010046 | OPTICS EXPRESS 10050

local maxima and the minimum intensity of a local maximum. Briefly, the method first sorts all the pixels in descending order according to their intensity and disregards pixels that are below Icut. This list of pixels are considered as candidates of local intensity maxima. Starting from the pixel with the highest intensity, the algorithm removes candidates from the list that are within a radial distance (r) typically set as the radius of a feature. Then the algorithm finds the next highest-intensity pixel, and repeats the same process until all pixels are exhausted. The remaining pixels are local intensity maxima within a circular region of the size of a feature. We apply this method to the correlation coefficient map shown in Fig. 2(b). This results in a set of pixels {x*,y*}w,wg which represent the most probable central locations of textural features in A for particular values of w and wg. The resulting centers are overlaid on top of the textural map T(w,wg), Fig. 2(c).

2.3 Statistical metrics Once locations of the various features are identified, we determine the most probable textural parameters, w and wg , that maximize the correlation coefficient across the entire image, w= w∋

∂ρ ( w, wg )

wg = wg ∋

= 0 and

∂w ∂ρ ( w, wg ) ∂wg

∂ 2 ρ ( w, wg )

6), both methods result in clean bandpass images (bright round features sitting on a dark background). However, for SNR < 2 (or SNRdB < 6), the C-G algorithm fails to provide good bandpass images, which leads to difficulty in localizing features reliably. TAM is less vulnerable to low signal-to-noise ratio. In Fig. 9(sixth column), we qualitatively compare the performance of the two methods by overlaying features identified by each one on top of uncorrupted images for direct comparison. It is clear that TAM provides much better feature detection and localization than the C-G algorithm for low-signal-to-noise ratio.

Fig. 10. Fidelity of centroid localization in simulated images of various signal-to-noise ratios. (a) The fraction of false detections as a function of signal-to-noise ratio. (b) Comparing detection accuracy as a function of signal-to-noise ratio between TAM and the C-G algorithm. Dotted lines designate the SNR below which the C-G algorithm fails.

Figure 10 compares quantitatively the performance of the two methods in terms of the occurrence of false identifications [Fig. 10(a)] and accuracy [Fig. 10(b)]. For images with SNR > 2 (or SNRdB > 6), both methods can localize features reliably. However, for SNR < 2 (or SNRdB < 6), the C-G algorithm starts to result in a number of false identifications. The false rate arises drastically as SNR drops below 1 (or SNRdB < 0), Fig. 10 (a, open symbols). In contrast, TAM provides 100% fidelity in feature identifications, even when SNR drops to 1 (or SNRdB = 0), and fidelity only drops abruptly for SNR < 0.6 (or SNRdB < −4.4), Fig. 10(a, filled symbols). Nevertheless, we find that our method still can provide over 85% correct detections even when SNR = 0.5 (or SNRdB = −6), at which point the C-G algorithm fails completely.

#206682 - $15.00 USD Received 18 Feb 2014; revised 25 Mar 2014; accepted 28 Mar 2014; published 18 Apr 2014 (C) 2014 OSA 21 April 2014 | Vol. 22, No. 8 | DOI:10.1364/OE.22.010046 | OPTICS EXPRESS 10058

In Fig. 10(b), we compare performance of TAM with C-G algorithm in terms of the accuracy in feature locations. When SNR≥2 (or SNRdB ≥ 6), TAM has similar (slightly better) performance as compared to the C-G algorithm. As SNR drops below 2 (or SNRdB < 6), the performance of both methods drops very quickly. However, TAM drops at a slower rate. More importantly, TAM can still provide an accuracy within ~1 pixel even when SNR < 0.5 (or SNRdB< −6), even though the unaltered C-G algorithm completely fails in this range. TAM clearly demonstrates its advantages for feature localization over the C-G algorithm both in terms of the occurrence of false identifications and accuracy.

Fig. 11. Comparing localization performance between TAM and the C-G algorithm applied to a 2D colloidal crystal. (a) Difference in detections between the C-G algorithm and TAM relative to TAM when adjusting in small steps the cutoff values of Iint, Rg and e to remove spurious features. Dotted line indicates where the C-G algorithm reaches its best agreement with TAM. (b)-(d) show the results of the C-G algorithm for this point. (b) Radial distribution functions calculated from particle coordinates obtained from TAM, the C-G algorithm and TAM with added random noise (see text for details). Different detections between the two algorithms are highlighted as large red circles overlaid on top of the raw image, with (c) false positives and (d) false negatives. The results of TAM are shown as small black circles. For visualization purpose, only a small fraction of the original image is shown.

4.3.2 Enhanced fidelity of centroid localization for unevenly illuminated, defect-laden images Here we explore the ability of TAM to deal with real experimental images in the presence of uneven illumination and optical artefacts. As an example, we extract centroids from the optical image shown in Fig. 1(c) and compare the performance of the TAM and C-G algorithms. Figure 11(a) shows the number of unique detections between the C-G algorithm and TAM, using the result of TAM as a reference since the ground truth is unknown for real images. In images of circular features, real features are usually clustered together in a parametric space of Iint vs Rg or Iint vs e. Defects or false features often have lower integrated intensity, large radius of gyration and high eccentricity. After running the C-G algorithm,

#206682 - $15.00 USD Received 18 Feb 2014; revised 25 Mar 2014; accepted 28 Mar 2014; published 18 Apr 2014 (C) 2014 OSA 21 April 2014 | Vol. 22, No. 8 | DOI:10.1364/OE.22.010046 | OPTICS EXPRESS 10059

thresholding values can be set for these parameters to help eliminate false detections [36]. We first set up (r, Icut) such that C-G algorithm can identify nearly all features detected by TAM (only 10 are missing out of 36751 features and we call them false negative detections). However, the algorithm also has approximately 2% detections that do not exist in TAM (false positive detections). We then adjust cutoff values for Iint (low bound), Rg (high bound) and e (high bound) in small steps to remove spurious features, Fig. 11(a). At the beginning up to step 3 (Iint,cut = 410, Rg,cut = 7.1 and ecut = 0.29), a fraction of false positive detections can be removed without inducing false negative detections. Afterwards, false positive detections can only be removed by sacrificing some features that are also detected in TAM (inducing more negative detections). In the end, we optimize these parameters at step 20 (Iint, cut = 490, Rg, cut = 6.5 and ecut = 0.19), beyond which there is no net gain in the total number of false detections. At this point, C-G algorithm misses 41 features detected by TAM and identifies 173 more features that are not detected by TAM. We then examine these different detections by overlaying false positive ones (features detected by C-G but not TAM) in Fig. 11(c) and false negative ones (features detected by TAM but not C-G) in Fig. 11(d). It is clear that the vast majority of these conflicting detections are false detections that cannot be removed by the C-G algorithm. We further compare the accuracy in centroid locations between the two methods. For this, the radial distribution function (RDF), which quantifies the probability distribution of interparticle separations, is an efficient metric. Errors in particle centroid location will lead to a decrease in the strong RDF peak at small distances associated with nearest neighbour particles [30,37]. We find that the RDF calculated based on the C-G algorithm has a lower peak compared to TAM, Fig. 11(b). We find that this decrease in relative performance of the C-G algorithm is equivalent to adding a Gaussian random noise with a variance of 0.18 pixels to the particle coordinates from TAM. From this, we estimate that TAM provides a better localization of features by 0.18 pixels, or 1.7% of the particle diameter.

Fig. 12. TAM and ImageJ analysis applied to computer simulated images of a binary colloidal system to extract size distribution. (a)-(d): computer generated images at decreased level of SNRs. Features identified from the two methods are overlaid on top of the original images, red dots for TAM and blue circles for ImageJ. Arrows indicate where ImageJ have false identifications. (e)-(f): Particle size distributions extracted by TAM and ImageJ, respectively.

#206682 - $15.00 USD Received 18 Feb 2014; revised 25 Mar 2014; accepted 28 Mar 2014; published 18 Apr 2014 (C) 2014 OSA 21 April 2014 | Vol. 22, No. 8 | DOI:10.1364/OE.22.010046 | OPTICS EXPRESS 10060

4.4 Extracting feature size distributions in polydisperse fluids Finally, we show that TAM can also be used to extract the size distribution of features in bidisperse or polydisperse systems. This is done, as described above, by locally optimizing the values of w and wg for each detected textural feature within the image. In doing so, we also compare the results of TAM to ImageJ, the most popular free software for analysing particle sizes [38]. The method first converts the original image to binary intensity by setting an arbitrary intensity threshold, and then uses a morphological algorithm to analyze the size distribution of objects. 4.4.1 Simulated images of a binary colloidal system We first apply our technique to computer generated images of a colloidal fluid of binary size distribution, Fig. 12. The size ratio of the two species is 2:1, which is set at w = 5 and 10. When there is no noise (SNR→∞), Fig. 12(a), our algorithm can precisely recover the expected intensity distribution of each particle when w is chosen as the size of each particle, Fig. 12(e, red bars). With increased noise level, Fig. 12(b)-12(d), our algorithm tends to bias the results to marginally larger feature sizes, with an increased width of the distribution, Fig. 12(e). This is expected due to the association of noisy pixels with true features. Nevertheless, the two populations are still well-separated, and we find errors in the average particle sizes of less than 2 pixels for SNR > 2.8 (or SNRdB > 8.9). For comparison, we also present results from ImageJ analysis, with features found overlaid on original images in Fig. 12(a)-12(d), and the distribution of feature size in Fig. 12(f). In general, ImageJ tends to bias the distribution towards smaller size as SNR decreases. ImageJ is vulnerable to incorrect identifications, which become more severe when SNR is low. This will significantly broaden the distribution of feature sizes, especially for the population of smaller features.

Fig. 13. Comparison of TAM and ImageJ for particle sizing in an optical image of a binary colloidal system. (a) Green and red circles indicate the sizes of w and wg for large particles, while purple and blue ones represent those of small particles. Feature centroids extracted from ImageJ are overlaid for comparison as yellow dots. Green arrows indicate false identifications from ImageJ. (b) The distribution of feature size extracted based on TAM (red bars) and ImageJ (yellow bars).

#206682 - $15.00 USD Received 18 Feb 2014; revised 25 Mar 2014; accepted 28 Mar 2014; published 18 Apr 2014 (C) 2014 OSA 21 April 2014 | Vol. 22, No. 8 | DOI:10.1364/OE.22.010046 | OPTICS EXPRESS 10061

4.4.2 Real images of a binary colloidal system With these limitations understood, we apply TAM to an optical image of an experimental binary system, Fig. 1(d), and present results in Fig. 13. We emphasize here that TAM essentially provides a Gaussian fit to the local intensity profile, which leaves the fit range, characterized by w, and the narrowness of the Gaussian, characterized by wg, as free parameters. The intensity profile will obviously be affected by many factors, such as noise level, over- or under-exposure, focus plane and the experimental system in question. Therefore, it requires caution to convert these parameters to the size of features. Here, it appears that wg works better than w in discriminating the two populations, Fig. 13(a, red and blue circles). The histogram of wg, Fig. 13(b, red bars), exhibits adequate separation of the two populations. ImageJ can also be used to segregate the two populations. However, ImageJ requires setting an arbitrary intensity threshold, which will inevitably lead to false identifications when there are global or local intensity variations, as shown in Fig. 13(a).

Fig. 14. TAM and ImageJ are applied to extract size distribution of features in a low-fidelity optical image in which features are not well defined. (a) Results from TAM are overlaid on top of the raw image, with green circles indicating the size of w and red ones indicating wg. Results from ImageJ are overlaid as yellow circles. (b) The probability distribution of w and wg are compared to the size distribution extracted from ImageJ.

4.4.3 Polydisperse system We lastly apply TAM to the low-fidelity image of Fig. 1(a), where there are even no welldefined features. We overlay the feature locations and sizes extracted from TAM and ImageJ in Fig. 14(a) and present the distribution of feature sizes in Fig. 14(b). We notice that some of the features have very large wg from TAM analysis, Fig. 14(b, red bars), which reflects the fact that the intensity profiles of these features are relatively flat (not sharp). In this case, we use w rather than wg as a measure of the size of a feature. As shown in Fig. 14(a, green circles), TAM can decompose the bi-continuous structure into circular blobs and provide a fairly good characterization of them throughout the image. In contrast, ImageJ lacks such ability, and tends to miss less-bright features, Fig. 14(a, yellow circles) and results in a much broader distribution of feature sizes, Fig. 14(b, yellow bars). 5. Conclusions and outlook

We have presented a new correlation-based image analysis, texture analysis microscopy (TAM). We have shown that TAM can be applied for enhanced visualization and structural analysis of low-fidelity images robustly under a number of various conditions. In addition, we show that the technique can serve as a subroutine for the commonly used Crocker-Grier

#206682 - $15.00 USD Received 18 Feb 2014; revised 25 Mar 2014; accepted 28 Mar 2014; published 18 Apr 2014 (C) 2014 OSA 21 April 2014 | Vol. 22, No. 8 | DOI:10.1364/OE.22.010046 | OPTICS EXPRESS 10062

algorithm without any changes to the original code, significantly improving its performance in feature localization under conditions that pose significant challenges, such as low-signalto-noise ratio, uneven illumination and artefacts. Although we only compare results here to the C-G algorithm, we expect to reach similar conclusions when comparing to other feature localization methods, since these conditions are common challenges in feature localization. In addition, we show that TAM can also be used to extract the structural correlations and size distribution of features for complex fluids, including systems with polydisperse and anisotropic objects. We also expect TAM to provide increased efficiency and accuracy for localization and orientation determination of colloids with arbitrary shape, particularly in 2D systems. For this, an anisotropic kernel that has both morphological and orientational parameters is required, and is the subject of ongoing investigation. This will allow textural topology, such as Voronoi tessellation and pair correlation, dynamics, and even microrheology to be studied for a much broader range of complex fluids than current application. Overall, TAM should be widely applicable in a number of contexts where highresolution imaging is unavailable including colloidal physics, biophysics of single molecules or cells, and super-resolution localization microscopy. As such, the technique holds potential for studying a number of structural and dynamical processes that occur in dense fluids, including phase separation and crystallization, as well as biomedical imaging and pattern recognition. Acknowledgments

We thank Megan T. Valentine from University of California Santa Barbara for stimulating discussions. We also acknowledge Dirk Aarts, Roel Dullens, Francois Lavergne, and Alice Thorneywork from University of Oxford for providing confocal and bright-field optical images. Funding for this work was provided by the National Science Foundation ( CBET1351371). Publication of this manuscript was kindly supported by the UCSB Open Access Fund.

#206682 - $15.00 USD Received 18 Feb 2014; revised 25 Mar 2014; accepted 28 Mar 2014; published 18 Apr 2014 (C) 2014 OSA 21 April 2014 | Vol. 22, No. 8 | DOI:10.1364/OE.22.010046 | OPTICS EXPRESS 10063

Texture analysis microscopy: quantifying structure in low-fidelity images of dense fluids.

Optical images are often corrupted by noise, low contrast, uneven illumination and artefacts, which may pose significant challenges to image analysis,...
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