Automatic Filler Dispersion Quantification in Microstructure Images of Bottom Ash Reinforced Polymer Composite

Fajar Astuti Hermawati, I Made Kastiawan, Muhyin


Distribution or dispersion analysis of polymer composite particle fillers is crucial to determine the material's properties and strengths. Bottom ash was used as reinforcement particles in the polymer composite. Bottom ash particles have an irregular shape, porous structure, and rough texture surface. The characteristics cause difficulty in detecting and analyzing the particles in the microstructure image of the polymer composite. This study aimed to build a robust method to detect bottom ash particles in the polymer composite automatically through microstructure image observation. The proposed method can also identify the agglomerated bottom ash particles, separate them, and analyze the distribution. Firstly, an image enhancement technique is applied to eliminate noise in the input image. A multi-level fuzzy segmentation method is implemented to obtain the filler particles region. Each particle region obtained is examined whether it is a touching particle and split it using an edge detection-based method. Before implementing the edge detection, the void filler algorithm is applied. We used a Prewitt edge detector that combines filling gaps between two broken segments using a round mask. The logic difference operation between the whole area and the resulting edge area is implemented to separate the touching particle region. At finally, the quantification of filler dispersion is carried out. To investigate the performance of the splitting method, we compared it with the watershed method.  In experiments, this touching particle splitting method can separate agglomeration particles with more than 90% accuracy. This study has two-fold novelties. Firstly, this pioneering study automatically identifies and quantifies filler particles with irregular shapes using image processing techniques. Second, the proposed splitting method has better performance than the watershed method used in previous studies.


Keywords: image processing, dispersion analysis, bottom ash particles, agglomeration particle, microstructure image.



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NGO T.-D. Introduction to Composite Materials. In: NGO T.-D. (ed.) Composite and Nanocomposite Materials: From Knowledge to Industrial Applications. IntechOpen, 2020: 1–27.

ANANE-FENIN K., AKINLABI E.T., and PERRY N. Quantification of nanoparticle dispersion within polymer matrix using gap statistics. Materials Research Express, 2019, 6: 075310.

KARASINSKI E. D. N., SASSE F. D., and COELHO L. A. F. Multifractal analysis of particle dispersion and interphase percolation in nanocomposites. Materials Research, 2018, 21(5): e20180265.

SANTOS R. M., MOULD S. T., FORMÁNEK P., PAIVA M. C., and COVAS J. A. Effects of particle size and surface chemistry on the dispersion of graphite nanoplates in polypropylene composites. Polymers, 2018, 10(2): 222.

ZACCARDI F., SANTONICOLA M. G., and LAURENZI S. Quantitative assessment of nanofiller dispersion based on grayscale image analysis: A case study on epoxy/carbon nanocomposites. Composites Part A: Applied Science and Manufacturing, 2018, 115: 302–310.

WANG G., YU D., KELKAR A. D., and ZHANG L. Electrospun nanofiber: Emerging reinforcing filler in polymer matrix composite materials. Progress in Polymer Science, 2017, 75: 73–107.

MARTINEZ R. F., ITURRONDOBEITIA M., IBARRETXE J., and GURAYA T. Methodology to classify the shape of reinforcement fillers: optimization, evaluation, comparison, and selection of models. Journal of Materials Science, 2017, 52: 569–580.

LI Z., GAO Y., MOON K.-S., YAO Y., TANNENBAUM A., and WONG C. P. Automatic quantification of filler dispersion in polymer composites. Polymer, 2012, 53: 1571–1580.

RAMZI N. I. R., SHAHIDAN S., MAAROF M. Z., and ALI N. Physical and Chemical Properties of Coal Bottom Ash (CBA) from Tanjung Bin Power Plant. IOP Conference Series: Materials Science and Engineering, 2016, 160: 012056.

KASTIAWAN I. M., SUTANTRA I. N., and SUTIKNO. Correlation of Holding Time and Bottom Ash Particle Size to Mechanical Properties of Polypropylene Composite. Key Engineering Materials, 2020, 867: 172–181.

KASTIAWAN I. M., SUTANTRA I. N., and SUTIKNO S. Effect of Bottom Ash Treatment and Process Variables on the Strength of Polypropylene Composites. International Review of Mechanical Engineering, 2020, 14: 324.

ESCHWEILER D., SPINA T. V., CHOUDHURY R. C., MEYEROWITZ E., CUNHA A., and STEGMAIER J. CNN-Based Preprocessing to Optimize Watershed-Based Cell Segmentation in 3D Confocal Microscopy Images. Proceedings of the IEEE 16th International Symposium on Biomedical Imaging, Venice, 2019, pp. 223–227.

ARIS T. A., NASIR A. S. A., and MUSTAFA W. A. Analysis of distance transforms for watershed segmentation on chronic leukaemia images. Journal of Telecommunication, Electronic and Computer Engineering, 2018, 10: 51–56.

ZHANG Y., & XU D. Improved watershed algorithm for cell image segmentation. Advanced Materials Research, 2012, 546–547: 464–468.

KORNILOV A. S., & SAFONOV I. V. An overview of watershed algorithm implementations in open source libraries. Journal of Imaging, 2018, 4(10): 123.

JI X. Q., LI Y., CHENG J. Z., YU Y., and WANG M. Cell image segmentation based on an improved watershed algorithm. Proceedings of the 8th International Congress on Image and Signal Processing, Shenyang, 2015, pp. 433–437.

KOWAL M., ŻEJMO M., SKOBEL M., KORBICZ J., and MONCZAK R. Cell Nuclei Segmentation in Cytological Images Using Convolutional Neural Network and Seeded Watershed Algorithm. Journal of Digital Imaging, 2020, 33: 231–242.

HERMAWATI F. A., KASTIAWAN I. M., and MUHYIN. Digital Microscopy Image Enhancement Technique for Microstructure Image Analysis of Bottom Ash Particle Polymer Composites. In: PARINOV I., CHANG S. H., and LONG B. (eds.) Advanced Materials. Springer Proceedings in Materials, Vol. 6. Springer, Cham, 2020: 235–244.

SARKAR S., PAUL S., BURMAN R., DAS S., and CHAUDHURI S. S. A Fuzzy Entropy Based Multi-Level Image Thresholding Using Differential Evolution. In: PANIGRAHI B., SUGANTHAN P., and DAS S. (eds.) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2014. Lecture Notes in Computer Science, Vol. 8947. Springer, Cham, 2015: 386–395.

NAIDU M. S. R., KUMAR P. R., and CHIRANJEEVI K. Shannon and Fuzzy entropy based evolutionary image thresholding for image segmentation. Alexandria Engineering Journal, 2018, 57: 1643–1655.

HERMAWATI F. A., TJANDRASA H., SUGIONO, SARI G. I. P., and AZIS A. Automatic femur length measurement for fetal ultrasound image using localizing region-based active contour method. Journal of Physics: Conference Series, 2019, 1230: 012002.

SOMAWIRATAA I. K., WIDODOA K. A., ACHMADIA S., and UTAMININGRUM F. Road Detection Based on Statistical Analysis. Journal of Hunan University Natural Sciences, 2020, 47(12): 57–64.

HERMAWATI F. A., TJANDRASA H., and SUCIATI N. Hybrid Speckle Noise Reduction Method for Abdominal Circumference Segmentation of Fetal Ultrasound Images. International Journal of Electrical and Computer Engineering, 2018, 8: 1747–1757.

UMAIR M., HASHMANI M. A., and KEIICHI H. Rough-Sea-Horizon-Line Detection using a Novel Color Clustering and Least Squares Regression Method. Journal of Hunan University Natural Sciences, 2020, 47(12): 133–145.

UDUPA J. K., LEBLANC V. R., ZHUGE Y., IMIELINSKA C., SCHMIDT H., CURRIE L. M., HIRSCH B. E., and WOODBURN J. A Framework for Evaluating Image Segmentation Algorithms. Computerized Medical Imaging and Graphics, 2006, 30: 75–87.


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