Crystal Size Distribution With Image Analysis Tool Assignment Sample

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Introduction of Crystal Size Distribution With Image Analysis Tool Assignment

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The optimal crystal distribution is very important in various industrial processing such as the pharmaceutical industry. Due to the ease of evaluating multiple particle dimensions such as size, and color (RGB) analysis, shape, processing of the digital images is rapidly being employed in many sectors such as food processing, medical research, particle technology, powder industries, cement industries, and so on. It uses program-based methods to conduct image analysis on digital pictures. To apply to the data input, a considerably wider selection of algorithms may be constructed. Image analysis has the advantage of being able to be represented in the shape of multifunctional networks. Appropriate particle size measuring or picture processing necessitates accuracy in noise reduction, edge detection, separation of contacting and overlaying particles, and so on. However, features created in image editing software like ImageJ are unable to measure accurately the exact shape and size of the crystal. 


ImageJ is a free software platform for image processing built-in Java that was created by the National Institutes of Health in the United States. Built on an open-source platform, it includes a variety of additional tools such as macros and plugins. Its constructed editor and translator are used to create a plugin for data capture as well as a plugin for data processing and analysis. Image processing challenges may be solved using self-developed and customized plugins, ranging from live-cell photography to radiographic image analysis, various image centralized data compare to computerized MAPI3 solutions. The plugins for ImageJ have rendered it a useful framework for learning image analysis (Lormand et al. 2018). It may be operated and maintained on an online application, incorporating downloading applications, as well as on any computer that has a Java 5 or later virtual environment. The processing of images is the most important stage which involves segregation and filtration for the overlapped particles. The following steps are needed to do the image processing in this image processing tool.

  1. Acquisition of image - Different images is analyzed and processed by a various process which involves:
  2. Filtration

i.Gaussian Blur

  1. Median
  2. Segmentation -
  3. Skeletonization through Influence Zone
  4. Thresholding through histogram

iii. Watershed segmentation

Filtration approaches such as Median and Gaussian Blur, which generate a new image based on the median function and Gaussian function whose values are determined from the main image, perform well. Following that, the newly formed picture is subtracted from the old image to establish a new picture (Meija et al. 2020). This is then transformed into a binary picture, and the components that are contacting or overlaying are segmented. So my focus would have been on the filtering approach, namely enhancing the Watershed segmentation by building a Java plug-in.

Watershed segmentation

This is frequently utilized and produces reliable results for practically all microstructures. The stages are described:

  • Transform the picture into a digital image
  • The selected pixels are differentiated with respect to the distance from the wider area
  • All nanoparticles creating upper limits in the 'range map' picture
  • Peaks thus obtained must be taken as seeding for the appropriate processing of the binary image (Berrezueta et al. 2019).
  • Extension utilizing thickness with the requirement that adjoining areas stay disconnected until the full image is completely filled

However, due to the underlying presupposition in this theory based on the watersheds, it's been discovered that this approach failed to divide the extremely uneven nanoparticles.

Results and discussion

The above graph is created from the data of UV – VIS spectrum. From the above graph it is show3n that the value of wavelengths () is 755 nm.

The measuring of a particle's band gap is critical in the nonmaterial sectors. In this research, it shows how to calculate a particle's band gap using its absorption spectrum of UV – VIS. The difference in energy between the conduction band and the valence band is referred to as the "band gap"; electrons can move from one energy band to another energy (Meng et al. 2018). The energy of the band gap is the minimal degree of energy required for a particle to shift from a valence band to a conduction band.

The energy band gap E = h*c/

Here h = 6.626 x10^−34 JS, c = 3 * 10^8 ms^-1

Therefore E = 1.2* 10^−6/ ,eV

E = 1.59 eV

The above picture shows the area and length distribution of the crystal at 80 degree Celsius. It is shown that the mean value of the data area range from 3.641 to 6.554 is 4.338 and the bandwidth is 1.456. The min data is 3.6414 where as the maximum value is 6.554. The mean value of the data area range from 11.098 to 20 is 13.131 and the bandwidth is 4.451. The min data is 11.098 where as the maximum value is 20 (Londoño-Restrepo et al. 2019).

The above picture shows the area and length distribution of the crystal at 100 degree Celsius. It is shown that the mean value of the data area range from 2.497 to 3.746 is 3.174 and the bandwidth is 0.624. The min data is 2.497 where as the maximum value is 3.746. The mean value of the data area range from 7.531 to 11.198 is 9.543 and the bandwidth is 1.834. The min data is 7.531 where as the maximum value is 11.198.

The above picture shows the area and length distribution of the crystal at 120 degree Celsius. It is shown that the mean value of the data area range from 4.993 to 3.746 is 6.138 and the bandwidth is 0.572. The mean value of the data area range from 15.118 to 18.573 is 17.286 and the bandwidth is 1.728.


Image analysis is a highly cost-effective and convenient way of measuring numerous physical attributes such as particle shape, size, composition, color, and texture. There are several systems accessible for image analysis, many of which are free to use. One of them is ImageJ, which really is free to use and performs well enough so for a range of sample types in general, and spherical shapes in specific. As a result, this research presents a way to overcome the present watershed segmentation method's shortcomings. It was discovered that including the previous section watershed segmentation performed quite well for non-spherical nanoparticles but did not produce as accurate results for very irregular nanoparticles. The calculated wavelength is 755nm and the energy band gap is 1.59 eV.



Meng, Y., Zhang, Z., Yin, H. and Ma, T., 2018. Automatic detection of particle size distribution by image analysis based on local adaptive canny edge detection and modified circular Hough transform. Micron, 106, pp.34-41.

Krause, S., Bon, V., Senkovska, I., Többens, D.M., Wallacher, D., Pillai, R.S., Maurin, G. and Kaskel, S., 2018. The effect of crystallite size on pressure amplification in switchable porous solids. Nature communications, 9(1), pp.1-8.

Elango, R., Demortière, A., De Andrade, V., Morcrette, M. and Seznec, V., 2018. Thick binder?free electrodes for Li–ion battery fabricated using templating approach and spark plasma sintering reveals high areal capacity. Advanced Energy Materials, 8(15), p.1703031.

Kumar, P.S., Korving, L., Keesman, K.J., van Loosdrecht, M.C. and Witkamp, G.J., 2019. Effect of pore size distribution and particle size of porous metal oxides on phosphate adsorption capacity and kinetics. Chemical Engineering Journal, 358, pp.160-169.

Londoño-Restrepo, S.M., Jeronimo-Cruz, R., Millán-Malo, B.M., Rivera-Muñoz, E.M. and Rodriguez-García, M.E., 2019. Effect of the nano crystal size on the X-ray diffraction patterns of biogenic hydroxyapatite from human, bovine, and porcine bones. Scientific reports, 9(1), pp.1-12.

Lormand, C., Zellmer, G.F., Németh, K., Kilgour, G., Mead, S., Palmer, A.S., Sakamoto, N., Yurimoto, H. and Moebis, A., 2018. Weka trainable segmentation plugin in ImageJ: a semi-automatic tool applied to crystal size distributions of microlites in volcanic rocks. Microscopy and Microanalysis, 24(6), pp.667-675.

Meija, J., Bushell, M., Couillard, M., Beck, S., Bonevich, J., Cui, K., Foster, J., Will, J., Fox, D., Cho, W. and Heidelmann, M., 2020. Particle size distributions for cellulose nanocrystals measured by transmission electron microscopy: an interlaboratory comparison. Analytical chemistry, 92(19), pp.13434-13442.

Berrezueta, E., Domínguez-Cuesta, M.J. and Rodríguez-Rey, Á., 2019. Semi-automated procedure of digitalization and study of rock thin section porosity applying optical image analysis tools. Computers & Geosciences, 124, pp.14-26.

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