Additionally, the histogram of the average distances of the closest neighbors provides a quantitative measure of how uniformly the feature of interest is spatially distributed. 1 0 obj At runtime, the plugin requires ImageJ to be running, https://github.com/sedmorteza/ND/blob/master/ND_.java. 3 0 obj One scenario that may lead to an inaccurate estimation of particle/pore distances might occur when particles/pores are too close to each other. While the size of particles can be estimated using the built-in Analyze Particle plugin in ImageJ, the spacing between the features cannot be estimated. 2). High-throughput characterisation of osteogenic differentiation of human mesenchymal stem cells using pore size gradients on porous alumina. Biomaterials 26(27): 547491, DOI:https://doi.org/10.1016/j.biomaterials.2005.02.002, Peng-Yuan, W, Lauren, RC, Helmut, T, Wei-Bor, T and Nicolas, HV (2013). %PDF-1.4 %%EOF Although ImageJ is taking center stage as a reliable open source and user-friendly tool for image processing in the biomedical field, image analysis is done either manually or by using propriety software associated with microscope companies or other commercial software whenever specialized plugins for ImageJ are not available. The plugin was written in Java, using the ImageJ built-in Java compiler. ND adds to the built-in functionalities of ImageJ to provide a fast and user-friendly method to better characterize the porosity of these scaffolds. Threshold the image to make the feature of interest evident (Image -> Adjust -> Threshold), Run the built-in Analyze Particles (Analyze -> Analyze Particles). Porosity of 3D biomaterial scaffolds and osteogenesis. Input window to take the coordination number of interest to the user (top) and the generated result table listing the average distance of each particle from its neighbors, nearest neighbor distance, and the average wall thickness of its neighbors (bottom). Journal of Biomedical Materials Research A 100(12): 32763286, DOI:https://doi.org/10.1002/jbm.a.34273, Oh, S, Brammer, KS, Li, YS, Teng, D, Engler, AJ, Chien, S and Jin, S (2009). Results are shown in a new result table, which contain the distance of the closest neighbor to each particle, the average wall thickness, and the average distance of the closest neighbors. hH;c'\8]w}oxLh`RC2eSL)f-Nj&(aP:&LL^J*X|[Z3 pL ND plugin running. hbbd```b``: "@$f"Y2@$&/El(Xo(4| 6MDHn,[,C2 GG7$tL8 30q0 In an electro-spun fiber mat, nano or micro sized fibers are stacked to form a mesh structure. stream Estimation of particle spacing of a particle with its neighboring particles is performed as follows: Scanning electron micrograph of a tubular scaffold used in tissue engineering (left) and its segmentation after using built-in Analyze Particles ImageJ plugin (right). <> In this situation a slight change in the values used for thresholding the image might lead to merging of two neighboring particles/pores. The ND Plugin functions as expected and results were manually verified. The user may run Analyze -> Analyze Particles built-in Plugin to generate a Results table needed by the ND Plugin. Fig. endstream endobj startxref ND is an image processing plugin that can be used to calculate the average size and distance between particles and their closest neighbors in many-particle systems. Functional testing, load testing and end-to-end testing were carried out in Microsoft Windows 7- 64 bit. In randomly packed systems, coordination number depends on the visual perception and can be lower or higher. The ND_.java file should be compiled for the first time using Plugins -> Compile and Run ..On restarting ImageJ, the plugin will appear under the menu Plugins. xXmoF>Er9qhCUU!\ N1\5!^%EFbfxK%!ngp`)2u;0$ EL7/A}HE(Jk9Yydt,z}L2N\ Proceedings of National Academy of Sciences 106(7): 21302135, DOI:https://doi.org/10.1073/pnas.0813200106, Karageorgiou, V and Kaplan, D (2005). By using the procedure mentioned above to threshold images from electro-spun fiber mats and running the built-in Analyze Particle plugin, one can generate a result table containing the radii of pores. Alternatively, in electro-spun fiber mats, ND can be used to estimate the average pore size as well as the average fiber size and distribution. Stem cell fate dictated solely by altered nanotube dimension. u=>._@:pWmvwcDxoDt6 thDL#H_*\r!Jg c-b6 bhaGG`G17d"G/VC@pRO?~ucJ 1)]p!/ %,''pN-~wjwlM^|ax 7BtA* Therefore, the limiting factor in the accuracy/precision of the ND plugin is the accuracy/precision data generated in the results table. The structure is highly porous with varying pore sizes. xYIF_s{J.x4 63 0 obj <>stream ND is particularly useful in analysis of porous synthetic and natural constructs (known as scaffolds) that are commonly used in the field of regenerative medicine and tissue engineering. It is written in Java and implemented in ImageJ, the open source Java-based software developed by National Institute of Health (NIH). A circle is fit on each particle with the center (X,Y) and radius r. The spacing (wall thickness) between a pair of particles (d) is calculated as: The distances of each particle with all the other particles is stored in an array and sorted. The level of accuracy/precision of the software when compared to manual verification is solely dependent on the threshold settings and the parameters set in Analyze Particle built-in plugin before running the ND plugin. Messages will be displayed in case any of the above conditions are not met and users will be directed on how to proceed. hb``b``Ja 7P#0p4 IA1C(?3tB,s`z3A8UU3B-h[$Z0 #W Therefore, characterization of fabricated scaffolds using microscopy techniques and subsequent processing of the images is an essential step in correlating a specific cell-scaffold response with an effective scaffold design. 2 0 obj endobj Prior to running the ND plugin, an image should be opened first and the Results table must have records including centroid coordinates of the particles that need to be analyzed. Depending on the coordination number of interest, average of the distances is calculated. The centroid coordinates of each particle (X,Y) is derived from the result table of the built-in Analyse Particles plugin. 0 ImageJ can be downloaded from http://rsbweb.nih.gov/ij. %PDF-1.5 % '8X8&eq8dGTvS&7TSTNWL$} Iu. <>>> Channeled scaffolds implanted in adult rat brain. Variants like Fiji (http://fiji.sc/) may also be used. However, the data required for OBC is stored in the matrix named dist in the code and can be used accordingly for other applications such as OBC. 38 0 obj <>/Filter/FlateDecode/ID[<126805CE0A339F4093D9A3138AA0DA44>]/Index[10 54]/Info 9 0 R/Length 119/Prev 137001/Root 11 0 R/Size 64/Type/XRef/W[1 3 1]>>stream Alternatively, the already compiled file (ND_.class) can be downloaded and placed in the Plugin folder of ImageJ installation. 4 0 obj These steps are as follows: ImageJ runs and is used on different versions of Unix, Mac OS X, and Windows. 2 0 obj endobj In OBC, the nearest neighbor distances (NND) from one point pattern to the other is plotted as a histogram and then a threshold distance is set (typically, the diffraction-limited resolution of ~ 200 nm), so that all points with a NND less than the threshold is considered co-localized. The architecture of the porous scaffolds including pore size distribution and density dramatically affects their interaction with biological cells and mechanical properties of the scaffold [1, 2, 3, 4, 5]. The plugin uses an image of the sample as input and outputs a result table listing the average distance of each particle from its neighbors, nearest neighbor distance, and the average wall thickness of its neighbors. ND plugin can be modified to provide this functionality. In a close packed configuration of particles/fibers having a circular cross section in 2D space there are 6 immediate neighbors surrounding each particle. hX[O;+~=JR -59#EyXV endstream endobj 11 0 obj <> endobj 12 0 obj <> endobj 13 0 obj <>stream <> Running the ND plugin subsequently will estimate the wall thickness between pores, which in this case will be the thickness of fibers forming a pore. stream ?t 'Q ]Ur>toz"PKl!>+QQz*\&W< Biomaterials 32(21): 48304839, DOI:https://doi.org/10.1016/j.biomaterials.2011.03.037, Martnez-Ramos, C, Valls-Lluch, A, Verdugo, JM, Ribelles, JL, Barcia Albacar, JA, Orts, AB, Soria Lpez, JM and Pradas, MM (2012). Therefore, with regard to values available in the results table, the ND plugin compared with manual calculation is completely accurate. The main usage of the ND plugin is to analyze the size and spacing of particles/pores/tubules located randomly in a sample. ND, which stands for nearest distances, is an ImageJ plugin that was developed to calculate the average size and distance between pores and their nearest neighbors in a porous scaffold by using images of the scaffold as input. To install the plugin, ImageJ must be installed first. <>/ExtGState<>/XObject<>/ProcSet[/PDF/Text/ImageB/ImageC/ImageI] >>/Annots[ 19 0 R] /MediaBox[ 0 0 612 792] /Contents 4 0 R/Group<>/Tabs/S/StructParents 0>> endobj %PDF-1.5 While the current code calculates the distances of all the neighboring particles from each particle, the ND plugin only uses the nearest neighbors up to maximum of 6, which corresponds to a close packed configuration into the subsequent averaging and display windows. The number of neighbors to be accounted for in calculating the averages is set by the user. ND is an example of a specialized plugin that builds upon the built-in functionalities of ImageJ, and contributes to the collective efforts to enrich its library of available plugins and advances its versatility in image processing. The result table which outputs the average distance between tubules, the average wall thickness, and the distance of the closest neighbor distance for each tubule is shown in Fig. Architecture of these scaffolds including pore size and density significantly affects the behavior and fate of cells cultured on them.

?)jL$POK BN"eE%(DPH TwGtBjz }PH>|)aZ. Q=QI3AfDU{KhWi-~&bX\# ! (Centroid and Fit ellipse must be checked under Analyze -> Set Measurements). _ Scott, JB, Afshari, M, Kotek, R and Saul, JM (2011). 1 shows an example of a tubular scaffold used in tissue engineering, which was analyzed using ND plugin. The promotion of axon extension in vitro using polymer-templated fibrin scaffolds. ACCN0s*vY>I6Ghy` H6o?4M-ty|z@O_ l.n/wY_OoDvvp`>3^L on4E=={M$ql|A"P .C % A common measure used in biology is object-based co-localization (OBC). 10 0 obj <> endobj XTP^_#'po[ZsF_HE|zCH%rD.46N1+#7$#!.Kl2R3gj,`/y2E|I.j z*X3{djANB93|=tv94rYqw[y &8-5dUi The main usage of the ND plugin is to analyze the wall thickness and average spacing of particles/pores/tubules located randomly in a sample. O4 S\ K_2tIRN 2:6TrVQWFe9n>~.Gf1> Ns>0} gzxJ}dWOWBf4N0oVCoZJXre=4TD%KmA-w1lcgIkmZ::b4m~#*b895\6XKC"QLPZS]QF];>*@q^ ;uo|2ME%YI6:/Uxb[Ty88N.^Q+qhMZpp4nj7$ sgK|EzXy{"I#wj . Initially, preliminary steps need to be taken for the plugin to do its function. <> Biomaterials Science 1(9): 924932, DOI:https://doi.org/10.1039/C3BM60026B, https://doi.org/10.1016/j.biomaterials.2011.03.037, https://doi.org/10.1016/j.biomaterials.2005.02.002. ND_.java file should be downloaded to the plugins folder of the ImageJ installation. % ND provides this ability for the user to calculate the spacing between each particle/pore and its n closest neighbors with n being the coordination number of interest (Fig. 7%2