Shape identification and particles size distribution from basic shape parameters using ImageJ

• Published in: Computers and electronics in agriculture 2008-10, Vol.63 (2), p.168-182
• Main Authors: Igathinathane, C., Pordesimo, L.O., Columbus, E.P., Batchelor, W.D., Methuku, S.R.
• Format: Article
• Language: English
• Subjects: Agronomy. Soil science and plant productions; automatic detection; Biological and medical sciences; computer analysis; digital images; food analysis; food composition; Fundamental and applied biological sciences. Psychology; Image processing; ImageJ; ImageJ plugin; Machine vision; particle size distribution; Physical properties; Plugin; shape; Shape identification
• ISSN: 0168-1699; 1872-7107
• DOI: 10.1016/j.compag.2008.02.007

Quick and accurate particle size distribution analysis is desirable in various technical fields that handle granular or particulate materials including size reduction. We developed an ImageJ plugin that extracts the dimensions from a digital image of disjoint particles after identifying their shapes and determines their particles size distribution. We established that the major and minor axes of ImageJ fitted ellipse along with the developed correction factors efficiently determined dimensions of particles. This paper describes the plugin development and its application to food grains and ground biomass. Using computer generated geometrical shapes as reference objects, a shape identification strategy that addresses common geometric shapes such as square, inclined square, rectangle, inclined rectangle, circle, ellipse, and inclined ellipse was developed. The strategy used only three newly defined shape parameters to identify objects, such as reciprocal aspect ratio, rectangularity, and feret major axis ratio from the standard outputs generated by ImageJ. Evaluation of effects of the particles shape, size, and orientation on the deviation from the reference particle's length and width indicated that the mean absolute deviations of all these factors were less than 1.3%. Developed plugin was applied successfully to analyze the dimensions and size distribution of food grains and ground Miscanthus particles images. The plugin produced quick and accurate size distribution of particles from digital images and can be applied to variety of particle analysis applications.