See Fig. The first step to image analysis is segmenting the image into foreground and background. Section 2. Generally, images were segmented with either global, local, machine learning or edge detection methods and then noise was removed and line smoothing was performed on resulting images.
The Super Pixel approach yields a single fiber diameter for each image and does not give a histogram. To obtain this calculation, white pixels from either digital synthetic or segmented images were summed for total fiber area in each image.
Additionally, two different centerlines were calculated for nanofibers in each image. The first centerline was determined using a thinning algorithm developed by Zhang and Suen[ 55 ] which is sensitive to changes in the fiber surface and results in branches that are not necessarily fibers sensitive centerline SC determination.
A second centerline was determined using a Voronoi spatial tessellation[ 56 ] which was built based on connected clusters of black background pixels and is insensitive to fiber surface morphology insensitive centerline IC determination. The length of the centerlines was calculated and the total area of fibers was divided by either the SC or IC length.
The Super Pixel name was chosen because the fiber area in pixels was divided by the centerline lengths in pixels, producing a unit-less value that is equivalent to a transformed larger pixel unit, equivalent to the mean fiber diameter. Using uncorrected centerline lengths to determine fiber diameter gave an overly large diameter value, and thus an intersection correction step was introduced which removed all intersections from the image.
Intersection correction takes the length of either the SC or IC and subtracts a radius value obtained from first approximation of the diameter as determined without intersection correction for each three-point intersection or a diameter value obtained from first approximation for each four-point intersection of the fibers. Intersections of each centerline were found using the algorithm developed by Arganda-Carreras et al. A new diameter was calculated using the new intersection-subtracted length and the total fiber area.
A thousandth of a pixel was chosen to drop the error to below 0. This value can be converted to intersections per unit area by the user. The intersections for the SC method were used for this calculation because the IC method was found to frequently miss branches of fibers if they did not segment mesh holes completely.
To obtain the distribution of fiber diameters, the segmented image was transformed with a Euclidian distance transformation algorithm[ 58 ]. The resulting image is a greyscale image rather than black and white. The SC sensitive centerline determination was overlaid on top of the distance transformed image.
At each intersection of the centerline, the greyscale value was found and radius values within that range were subtracted out from the centerline this step removes intersections. The greyscale values under the remaining centerline were obtained and multiplied by 2 to get the value of all diameters not in an intersection area. The subsequent histogram of greyscale values was found and placed in a ". The SC was used because it gave the more correct answer when used to analyze digital synthetic images with known intersection densities.
It was hypothesized that using the SC produced an answer that was closer to known values than when using IC because SC removes more intersections than IC and because SC has a higher likelihood of eliminating pixels that have a higher value than the true radius.
As a convenience to the user, mesh hole analysis was included in the plugin using a previously developed algorithm [ 35 ]. The particle analysis was used to generate mesh hole histograms, mean mesh hole area produced by averaging all cluster areas , and percent mesh hole produced by taking the total number of black pixels and dividing it by the total image resolution.
As a convenience to the user, fiber orientation analysis was also included in DiameterJ using OrientationJ, an established algorithm for ImageJ. The Fourier gradient was used with a Gaussian window equal in size to the mean fiber diameter as determined by the DiameterJ Super Pixel algorithm. The subsequent frequency histogram of fiber orientation was reported. Explanations of how absolute and percent errors were calculated are described can be found in section 2.
These algorithms were validated using the digital synthetic images discussed above. The output from these algorithms was used to calculate normalized orientation index NOI , mesh hole size, porosity, intersection density and characteristic fiber length CFL. Examples of digital synthetic image sets can be seen in Fig. An analysis was performed on the digital synthetic images for the DiameterJ Super Pixel algorithm where centerlines determined by the IC and SC approaches were compared Table 1.
Generally, the IC derived diameter was smaller than the known diameter and the SC derived diameter was larger. Thus, the remainder of the analyses were performed using the average of the IC and SC diameter which was designated the Super Pixel diameter.
For Ordered-3D and Disordered-3D images, which had three line diameters, percent error generally increased with increasing mean line diameter and the Super Pixel algorithm performed better than did the Histogram or BoneJ.
In almost every digital synthetic image measured, BoneJ had a higher mean percent error than did Super Pixel or Histogram. Table 2 shows the mean error of the algorithms across all digital synthetic images with line diameters between 10px and px. An advantage of DiameterJ Histogram is that many fiber diameter measurements are made on each image enabling histograms of fiber diameter distributions to be generated and analyzed via peak-fitting Fig. Gaussian curves were fit to histograms of Multi-Dia.
The data and fits for the histograms as well as the original digital synthetic images can be seen in Figure 2 of reference [ 53 ]. For images with multiple line diameters that were close together multimodal peak-fitting became more difficult due to overlap between the peaks see six line diameter image in Fig. To measure the deviation of the peak-fits, the absolute Fig. The mean absolute error of the peak-fits was less than 0. In Fig. Measurements on diameter Multi-Dia. Thus, peaks should be separated by at least 3 px in order to enable accurate peak-fitting.
These results validate that Super Pixel and Histogram were able to effectively determine line diameters in digital synthetic images. FibreQuant was not designed to analyze segmented images black and white images and, thus, could not be validated on digital synthetic images. Thus, small gauge 48, 50, or 53 ga. S2 , Fig. The modes of the DiameterJ Histogram results shown in Fig. The histograms showed a tight dispersion of measured wire diameters with none of the Gaussian peak-fits having a standard deviation greater than 0.
The data and fits for the histograms as well as the original SEM images and their segments can be seen in Figure 3 of reference [ 53 ]. Analysis of wire micrographs with different measurement tools Super Pixel, Histogram, BoneJ, Manual, PrC revealed that each tool produced a similar amount of error when comparing measurements between the three sizes of single ga. The Super Pixel algorithm had a statistically higher error than the Manual measurement when measuring the 48 ga.
BoneJ had a statistically higher error when compared to any other tool across all three gauges. BoneJ included all fiber intersections in its analysis which most likely led to inflated average diameters as shown Fig. For mixed-ga. Taken together, these results indicate that Super Pixel and Histogram produced fiber diameter measurements from SEMs of steel wire that were equivalent to the measures produced using other measuring methodologies.
C, D Cumulative fiber diameter histograms generated by DiameterJ Histogram left and by human measure right. The mean and standard deviation in nm are given on the bars in E. Error bars are standard deviation. DiameterJ Histogram generated between and fiber diameter counts per image, depending on the image properties number of fibers present, diameter of fibers, etc.
Thus, the signal to noise of the histograms generated by DiameterJ Histogram is higher and more robust peak-fitting is possible. BoneJ calculated a diameter that was statistically higher than DiameterJ Histogram for monodispersed nanofibers.
The data and fits for the histograms as well as the original digital synthetic images can be seen in Figure 4 of reference [ 53 ]. Of note was the time taken to analyze images.
For manual measurement each analyst spent approximately 8 minutes per image leading to a total time of analysis of approximately 5 hours per analyst to generate data points for each of the six samples 48 ga. Compared to DiameterJ with included automated segmentation algorithms taking less than 40 minutes to analyze all six samples while generating tens of thousands of measurements per sample. In summary, these results demonstrate that DiameterJ algorithms agreed with manual measures of PLGA fiber diameter, but were 10s of times faster while providing s of times the data for peak fitting.
In order to enable flexibility, DiameterJ does not prescribe a segmentation algorithm and analyzes images after segmentation. SEM image properties vary widely between instruments, instrument settings, users, sample preparations and fiber compositions.
The best results can be obtained if the user selects the segmentation algorithm that works best for their image sets. For convenience, several segmentation tools have been packaged with DiameterJ to help users obtain an adequate segmentation for fiber analysis. These segmentation tools use 16 common algorithms to segment an SEM image and provides them to the user so that the best approach may be selected for fiber diameter analysis via DiameterJ. In addition, Fiji has more than 25 global and local segmentation algorithms, as well as several machine learning and edge detection algorithms built into its latest release.
Further, a tool has also been packaged with DiameterJ, for convenience, which allows the user to identify where in the image DiameterJ is obtaining any given fiber diameter measure. S4 shows further confirmation of the heterogeneity of segmentation algorithms when analyzing other SEM images. However, despite the differences in the segmentation, peak fitting of DiameterJ Histogram results for the 4 segmentations were consistent 0. These results suggest that DiameterJ Histogram fiber diameter measurement and analysis were relatively insensitive to the choice of segmentation algorithm.
Segmentation algorithms and analysis of fiber dia. A An SEM micrograph of 53 gauge wire dia. D Summary table of DiameterJ analysis of the segmented images. E Original SEM image of 53 ga. DiameterJ incorporates several previously published plugins including mesh hole size, fiber orientation [ 36 ], and fiber intersection density[ 57 ].
Prior to incorporation into DiameterJ, the Particle Analyzer algorithm and OrientationJ were validated using digital synthetic images Fig. Ordered-1D digital synthetic images were analyzed because their mesh hole size and fiber orientations were known. Finally, the method used for image segmentation induced greater variability on these additional fiber metrics than it had on fiber diameter Fig. While the fiber diameter measurements had a coeff.
The data that was analyzed for this analysis can be seen in Figure 5 of reference [ 53 ]. Two fundamentally distinct algorithms for determining fiber diameter, Super Pixel and Histogram, were included in DiameterJ so that the program could analyze a larger range of image types.
Super Pixel yields a single mean fiber diameter value from each image analyzed. In contrast, Histogram yields a fiber diameter measurement at every pixel along the length of every fiber, yielding thousands of data points for each image.
These large data sets enable fiber diameter distributions to be plotted and analyzed via peak-fitting; providing better confidence in fiber diameter measurements. However, because Histogram can show distributions of fiber diameter and because it can show multiple modes in a given image the Authors recommend using the Histogram algorithm with peak fitting for most use cases unless the User truly only needs a global mean of multi-modal fibers.
Additionally, when comparing this work to the results found by Stanger et al. DiameterJ appears to produce results that are not statistically different from that of all the commercially reviewed pieces of software in that study. For a more in-depth discussion of the underlying algorithmic reasons for the error trends shown in these Tables and Figures see the Supplemental Discussion of this report.
A primary reason that DiameterJ placed a priority on measurement of fiber diameter was because two-dimensional 2D images of tubular structures, such as fibers, can be used to determine the diameter of a tube. Sage, C. Bouten, F. Unser and N. Stergiopulos, Experimental investigation of collagen waviness and orientation in the arterial adventitia using confocal laser scanning microscopy, Biomechanics and modeling in mechanobiology, SpringerLink DOI: Nock, F.
Pattern Anal. A threshold selection method from gray-level histograms. Automatica ;—7. Image thresholding by minimizing the measures of fuzziness. Pattern Recognit ;— Minimum error thresholding.
Pattern Recognit ;—7. Characterization of the complete fiber network topology of planar fibrous tissues and scaffolds. Thinning Methodologies-A Comprehensive Survey. Commun ACM ;—9. Microsc Res Tech ;— Leymarie, M. Scale space approach to directional analysis of images.
This page was last modified on 24 January , at Privacy policy About ImageJ Imprint. Categories : Plugins Analysis. ImageJ 1. XXX DiameterJ v1. Source Code. Plugins Analysis. Summaries Folder. Super Pixel: The mean fiber diameter as calculated using a super pixel determination. A detailed explanation of how this algorithm works can be found above.
Histogram Mean: The mean fiber diameter as calculated by fitting a Gaussian Curve to the radius data and finding the curves mean value. Histogram SD: The standard dev. Fiber Length: The total length of the Fiber centerlines in the segmented image. Histograms Folder. See full list on the FreeDiameter website. Complete code is written from ground up. That means it has very less third party library dependency.
This makes code more stable and less prone to version mismatches. The support is quick and responsive. This ensure prompt bug fixing and help with the stack.
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