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Sparavigna, A. (2017). Image segmentation applied to micrographs of  microcellular plastics. PHILICA.COM Article number 953.

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Image segmentation applied to micrographs of microcellular plastics

Amelia Carolina Sparavignaunconfirmed user (Department of Applied Science and Technology, Politecnico di Torino)

Published in compu.philica.com

Abstract
The paper is proposing the use of a method of image segmentation to the study of microcellular plastics. The segmentation is based on a thresholding which creates a binary (black and white) image of the micrograph. The binary image is divided in super-pixels which correspond to the microcells of the material. From the areas of the super-pixels it is easy to evaluate the size of the cells.

Article body

 

Image segmentation applied to micrographs of  microcellular plastics

 

Amelia Carolina Sparavigna

Politecnico di Torino, Italy

 
Abstract The paper is proposing the use of a method of image segmentation to the study of  microcellular plastics. The segmentation is based on a thresholding which creates a binary (black and white) image of the micrograph. The binary image is divided in super-pixels which correspond to the microcells of the material. From the areas of the super-pixels it is easy to evaluate the size of the cells.     

Keywords— Microcell sizing, image segmentation, image processing, microcellular plastics.

 

Here we are proposing the use of the image segmentation, one of the fundamental methods of the image processing, to the study of microcellular plastics. The microcellular plastic, also known as microcellular foam, is a form of material fabricated to contain tiny bubbles less than 50 microns in size. The bubbles are formed by dissolving a gas under high pressure into a polymer, to obtain an almost uniform arrangement of the gas bubbles [1]. The plastic that is obtained has good mechanical properties, with a reduced use of material; in it, the density of the foam is ranging between 5% to 99%  of the pre-processed plastic [2].

Recently, researches made at the Indian Institute of Technology in Delhi have been published on new technologies based on ultrasounds for the development of high quality microcellular foams [3,4]. In [5], we can see a micrograph of a sample obtained by this Institute. It could be interesting to evaluate the distribution, in size, of the cells we observe in the micrograph. To this purpose, an image segmentation can be used. Actually, by means of an image segmentation we can obtain a segmented image made by super-pixels, that, in the case we are considering, correspond to the microcells of the plastic material. The evaluation of the area (in pixels) of the super-pixels give us the possibility of estimating the size of the cells.

In image processing, a segmentation is a process of partitioning an image into multiple sets of pixels, defined as super-pixels, in order to have a representation which is simpler than the original one and more useful to the following desired analyses [6-9]. The typical use of the image segmentation is that of locate objects, or domains, and boundaries among them. Specifically, the segmentation is a process of assigning a label to every pixel in an image, such that the pixels having the same label share certain characteristics [9]. As a consequence, the result of the segmentation is a set of "segments", also known as “super-pixels”, that are covering the whole image.

Several methods exist for segmentation, as we can appreciate from [9]. In [10], we have proposed a method based on thresholding, a method aimed to be used in the analysis of the vesicular textures, the textures we can encounter in geology, materials science and physics. The vesicular textures in images or micrographs are features that evidence the presence of cavities or holes in the samples; these textures look like those displayed by the volcanic rocks when are pitted with many cavities (known as vesicles) at their surfaces and inside. In such cases, the vesicles are made by gas escaping from cooling lava.

Another example of the use of the image segmentation has been discussed in [11], for the case of a natural honeycomb structure. In [11] we plotted the distribution of the super-pixels, and consequently, the frequencies of the areas of the cells that we see in the image of the honeycomb. In the Figure 1, the three steps of segmentation by thresholding are illustrated [10,11]. We start from the original image, converted in grey tones, as in the panel A of the Figure. Then we use GIMP (GNU Image Manipulation Program for X Windows systems) for a simple thresholding to have the black and white image, reproduced in panel B (in fact, a thresholding based on the entropy of image is also possible [12-14]). On the binary black and white image, we apply a Fortran program for segmentation [10]. In the panel C, we show the super-pixels having different colour tones. Since each black pixel of the original grey-tone image has the label of the super-pixel to which it belongs, we can easily do some calculations. For instance, we can calculate the number of the super-pixels. Then, we can give the number of pixels contained in each super-pixel. In this manner we can measure the area of each cell of the honeycomb lattice, as given in the panel D of the Figure 1.  Of course, we can do the same for the case of a microcellular plastic (Figure 2). The original image is the micrograph given in [5].

 

 

 

Figure 1: The grey-tone image (240 x 240 pixels) is given in the panel A (Courtesy: Audrius Meskauskas, Wikipedia). In B, the corresponding black and white image is given, as obtained after a thresholding. In C, the result of the segmentation is shown. Each super-pixel is given in different colour tone. In D, some cross-sectional area (in pixels) of the honeycomb cells are given.

 

Figure 2: The grey-tone image (240 x 240 pixels) is given in the left panel (Microcellular Plastic Micrograph developed at Indian Institute of Technology, Delhi. Courtesy: Gandhi.iitdelhi, Wikipedia). In the middle, the corresponding  black and white image obtained after a thresholding. On the right, the result of the segmentation is given. Each super-pixel is rendered in different colour tone.

 

 

As we can see from the example in the Figure 2, some small super-pixels can appear, due to the segmentation of the regions that we find between the large cells in the image. To reduce their role in the counting of the distribution of the super-pixels, the binary image we obtain after the thresholding of the grey-level image is further subjected to a processing. This processing is made by the GIMP generic filter  “dilate”, followed by the generic filter “erode” [15]. The segmentation we obtain is given in the Figure 3.

Figure 3: The whole micrograph of the Figure 2 and its segmentation. (Microcellular Plastic Micrograph developed at Indian Institute of Technology, Delhi. Courtesy: Gandhi.iitdelhi, Wikipedia). The area of the image is 600 x 384 pixels.

 

If we consider the segmentation and the super-pixels as given in the Figure 3, we can determine the distribution of the microcell cross-sections, by counting them according to their area (in pixels) within intervals spaced of 50 pixels. We obtain the distribution given in the Figure 4 (the super-pixels having an area less than 25 pixels are not considered).

 

Figure 4: Distribution of the super-pixels.

 

Figure 4 shows that a few polyhedra having a large size exist, but many polyhedra with a cross-section between 100 and 400 pixels are found in the micrograph. It means that we have a large number of cells with size ranging from 6 to 11 pixels (in the case that we have also a scale on the recorded micrograph, we can give the size in microns). In the Figure 5 and 6, we are proposing another example.

In this paper we have proposed the use of an image segmentation in the study of  microcellular plastics. The method of segmentation is based on thresholding. We have also filtered the image for avoiding a spurious number of very small super-pixels. Further studies are necessary to link the distribution of the super-pixels, and then of the microcells, to the mechanical properties of the material.

 

Figure 5: An image of microcellular plastic and its segmentation. (Microcellular Plastic Micrograph developed at Indian Institute of Technology, Delhi. Courtesy: Gandhi.iitdelhi, Wikipedia, https://commons.wikimedia.org/wiki/File:Cyclic_Microcellular_Foam.tif). The area of the image is 600 x 376 pixels.

 

Figure 6: Distribution  of the super-pixels.

 

References

[1] Suh, Nam P. (2003). Impact of microcellular plastics on industrial practice and academic research. Macromolecular Symposia. 201 (1): 187–202. DOI: 10.1002/masy.200351122. ISSN 1521-3900

[2] Microcellular Plastics Lab - University of Washington. faculty.washington.edu. Retrieved 2017-02-04.

[3] Abhishek Gandhi, Neelanchali Asija, Kumresh Kumar Gaur, Syed Javed Ahmad Rizvi, Vijay Tiwari, Naresh Bhatnagar (2013) . Ultrasound assisted cyclic solid-state foaming for fabricating ultra-low density porous acrylonitrile–butadiene–styrene foams. Materials Letters. 94 (94): 76–78. DOI: 10.1016/j.matlet.2012.12.024.

[4] Gandhi Abhishek, Neelanchali Asija, Hemant Chauhan, & Naresh Bhatnagar (2014). Ultrasound-Induced Nucleation in Microcellular Polymers. Journal of Applied Polymer Science. 131 (18). DOI: 10.1002/app.40742.

[5] Vv. Aa., Wikipedia, https://en.wikipedia.org/wiki/Microcellular_plastic. Retrieved 2017-02-04.

[6] Shapiro, L. G., & Stockman, G. C. (2001).  Computer vision, New Jersey, Prentice-Hall, ISBN 0-13-030796-3

[7] Pham, D. L., Xu, Chenyang, & Prince, J. L. (2000). Current methods in medical image segmentation. Annual Review of Biomedical Engineering. 2: 315–337. DOI: 10.1146/annurev.bioeng.2.1.315. PMID 11701515.

[8] Forghani, M., Forouzanfar, M., & Teshnehlab, M. (2010). Parameter optimization of improved fuzzy c-means clustering algorithm for brain MR image segmentation. Engineering Applications of Artificial Intelligence, 23 (2): 160–168.

[9] Vv. Aa. (2016). Wikipedia. https://en.wikipedia.org/wiki/Image_segmentation

[10] Sparavigna, A. (2016). A method for the segmentation of images based on thresholding and applied to vesicular textures. PHILICA Article number 889.

[11] Sparavigna, A. (2016). Analysis of a natural honeycomb by means of an image segmentation. PHILICA Article number 897.

[12] Sparavigna, A. C. (2015). Tsallis entropy in bi-level and multi-level image thresholding. International Journal of Sciences, 4(1), 40-49. DOI: 10.18483/ijsci.613

[13] Sparavigna, A. C. (2015). Shannon, Tsallis and Kaniadakis entropies in bi-level image thresholding. International Journal of Sciences 4(2), 35-43. DOI: 10.18483/ijsci.626

[14] Sparavigna, A. C. (2016).  The Tsallis Entropy and the Segmentation of Images. SSRN Electronic Journal. DOI: 10.2139/ssrn.2820565

[15] Details on GIMP filters “dilate” and “erode” at https://docs.gimp.org/en/plug-in-dilate.html and at  https://docs.gimp.org/2.2/en/plug-in-erode.html

 



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Sparavigna, A. (2017). Image segmentation applied to micrographs of microcellular plastics. PHILICA.COM Article number 953.


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