SLIDE 26 Abstract
Funding for this research was provided by the Center for Advanced Surface Engineering, under the National Science Foundation Grant No. IIA-1457888 and the Arkansas EPSCoR Program, ASSET III. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
Concluding Remark
Only a few seconds are required to find the center location of all circles that fit with in the fiber width by using the develop diameter finding toolbox, thus it is a very fast method than compare to the manual method. The validity of the diameter finding method is tested by comparing the result with the manual method and distance transform and skeletonization method, which proves that the develop toolbox is giving more accurate result compared to the distance transform and skeletonization technique. The result of the develop orientation finding toolboxes are validated by applying on the simulated images where the orientation distribution of the objects are known. These results prove that the develop Fourier method is giving accurate orientation of the aligned fibers and the gradient method can be used to find the orientation distribution of the randomly oriented fibers. Figure 3: (a) SEM image of Poly(ethersulfone) (PES)) fibers [1],(b) Input image is converted to binary image by using slider, Figure 8: (a) User interface of the develop orientation finding toolbox, SEM image of PLGA fibers [2] is selected for analyzing, (b) Orientation distribution of PLGA Fibers.
(a) (b)
Introduction Toolbox for Finding the Diameter Distribution of Fibers References Acknowledgement
[1] Katti DS, Robinson KW, Ko FK, Laurencin CT, “Bioresorbable nanofiber-based systems for wound healing and drug delivery: optimization of fabrication parameters,” Journal of Biomedical Materials Research Part B: Applied Biomaterials, Volume 70B, Issue 2, pages 286–296, 15 August 2004. [2] Croisier F., Duwezb A.-S., Jérômea C., Léonardc A.F., Werfd K.O., Dijkstrae P.J., Benninkd M.L., “Mechanical testing of Electrospun PCL Fibers,” Acta Biomaterialia, Volume 8, Issue 1, January 2012, Pages 218–224. [3] Chaudhuri B.B., Kundu Puluk, Sarkar Nirupam , “Detection and gradation of oriented texture,” Pattern Recognition Letters, Volume 14 Issue 2, Feb. 1993, Pages 147 – 153. Figure 2: (a) User interface of the develop diameter finding toolbox (b) Flowchart of the develop algorithm. The aim of tissue engineering is to repair or regenerate the damaged tissues instead of replacing them by developing biological substitutes that restore, maintain or improve tissue function. To achieve this aim, three dimensional porous scaffolds have been used extensively in tissue engineering to provide the appropriate environment for the regeneration of tissues and organs by mimicking the behavior and properties of natural extracellular matrix (ECM). For developing an efficient artificial ECM (aECM) or to mimic the native ECM architecture, it is very important to design a suitable scaffold where cells should be able to adhere, migrate and proliferate in order to regenerate the damaged tissues. Several studies have shown that structural properties of fibrous scaffolds such as diameter, orientation distribution of fibers have a pronounced influence on cell behavior. So, in this study, standalone image analysis toolboxes are generated to find the diameter and orientation distribution of fibers from the microscope images. Graphical user interface and deployment toolbox of Matlab software are used to generate the standalone image analysis toolboxes so that any untrained user can easily use the develop toolboxes without installing the Matlab software. The angular amplitude of FFT, 𝐵 𝜄 , is determined by summing the contribution from each pixel in the sub image: Finally, A(θ) was converted to Cartesian coordinate and eigenvalues are calculated for the following vector: Gradient Method: The image is divided into M x M sub regions. For each sub image (W), a 180 element array 𝐵𝜄
𝑋
(contained all angles between 0-179ⴰ) is defined and quantized in 1ⴰ intervals [3]. Figure 1: Basic principles of tissue engineering.
(a) (b) (a) (b) (a) (b)
Image Analysis Toolboxes for Finding the Diameter and Orientation Distribution of Fibrous Scaffold
Samia Sanjari & Brandon A. Kemp
Figure 4: (a) Circles fit with in the fiber width to find the diameter of fibers, (b) Diameter distribution of fibers. Simulated Image Analysis: Figure 5: (a) Image generated by using f(x,y)=sin(10πx),(b) Image generated by using f(x,y)=sin(10πx) rotated by 90 degree (b) Image generated by using f(x,y)=sin[2π(10x+16y)].
(c) (a) (b) Input Image Orientation (with respect to horizontal axis) (a) 0ⴰ (b) 89.99ⴰ (c) 44.4ⴰ
Table 1: Orientation of synthetic images Figure 6: User interface of the develop orientation finding toolbox, SEM image of PCL–gelatin ultrafine fibers [2] is selected for analyzing. Real Image Analysis: Simulated Image Analysis: Figure 7: (a) Randomly oriented lines ( Simulated image generated by using paint), (b) Orientation distribution
- f lines determined by using the gradient method.
(a) (b)
Toolbox for Finding the Orientation Distribution of Fibers
Fast Two Dimensional Fourier Analysis(FFT): Real Image Analysis:
