Fractal analysis of the pigs muscle tissue histostructure: a preliminary study

DOI: 10.32900/2312-8402-2019-121-146-156

Kramarenko Aleksandr Sergeevich,
ORCID ID: 0000-0002-2635-526X,
Kramarenko Sergei Sergeevich,
Doctor of Science,assistant professor,
ORCID ID: 0000-0001-5658-1244,
Lykhach Anna Vasil’evna,
PhD, assistant professor,
ORCID ID: 0000-0002-0472-6162,
Lykhach Vadim Yaroslavovich,
Doctor of Science,assistant professor,
ORCID ID: 0000-0002-9150-6730,
Mykolayiv National Agrarian University

Keywords: histological profile, longissimus dorsi muscle, pigs, fractal dimension


The aim of this study was to investigate differences in the histological profile of the longissimus dorsi muscle of different swine breeds – Large White (LW), Ukrainian meat (UM), Duroc (D) and Landrace (L).
A total of 100 purebred (LW×LW, UM×UM, D×D and LN×LN ) and crossbred pigs (LW×D, D×LW, LN×D, D×LN, LN×LW and LW×LN) were evaluated. Muscle fiber diameter and percentages of the muscle tissue and intramuscular fat content were investigated.
We model pig’s meat structure as a fractal, and assume the projected image of the histological profile can be described by a fractal dimension (FD) estimates. Fractal dimension, FD, measured by the box-counting method was used to quantize the histological outlines of the muscle tissue images. Fractal dimensions estimates were determined using special software Fractalyse – Fractal Analysis Software v. 2.4.1.
The average muscle fiber diameter in pigs ranged from 16 (D × LN) to 43 µ (LN × LW). The fractal dimension (FD) values range from 1.808 to 1.886 (R2 = 0.9938-0.9998). All feature parameters of the histological profile significantly correlated with fractal dimension.  A good correlation (r = 0.735; p = 0.015) was obtained between the mean fat content measured by histological analysis and a fractal dimension (FD) estimates in different pig genetic groups. All genetic groups of pigs can be attributed to three clusters characterized by different muscle tissue properties, based on the obtained the fractal dimensionestimates.
Fractal dimension estimates may be used for the pattern of the muscle fibers and intramuscular fat distribution estimated based on the histological profile images.


 1. Peretyatko, L. G. (2011). Osoblyvosti gistostruktury m’yazovoyi tkanyny poltavs’koyi m’yasnoyi porody svynej [The peculiarities of a histostructure of muscle tissue of the Poltava meat breed of pigs]. Svynarstvo – Pig breeding, 59, 25–27 [in Ukrainian].
2. Rybalko, V. P., & Floka, L. V. (2014). Histolohichna budova m’yaziv svyney chervonoyi bilopoyasoyi porody [Histiology structure of muscle of pigs of the Red White breed]. Svynarstvo – Pigbreeding, 65, 112–115 [in Ukrainian].
3. Birta, H. O. (2009). Histolohichni doslidzhennya naydovshoho m’yaza spyny svyney riznoho napryamu produktyvnosti [Histological studies of the longest muscle of the back of pigs of different directions of productivity]. Visnyk Poltavs’koyi derzhavnoyi ahrarnoyi akademiyi – Bulletin of Poltava state agrarian academy. Poltava, 1, 62–65 [in Ukrainian].
4. Ballerini, L., & Bocchi, L. (2001). A fractal approach to predict fat content in meat images. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis, (pp. 351–354). Pula, Croatia.
5. Мandelbrot, B. (2002). Fraktal’naya heometryya pryrod– The Fractal Geometry of Nature. Moscow :  Instytut komp’yuternykh issledovanyy [in Russian].
6. Chen, K., & Qin, C. (2008). Fractal feature analysis of beef marbling patterns. International Conference on Computer and Computing Technologies in Agriculture, (pp. 2177-2186). Beijing, China.
7. Chang, R., & Wei, Y., Ma, L., Wang, Y., Liu, H., & Song, M. (2010). The judgment of beef marble texture based on the MATLAB image processing technology. International Conference on Computer and Computing Technologies in Agriculture, (pp. 106-112). Nanchang, China.
8. Chen, J., & Liu, M., & Zong, L. (2012). The fractal dimension research of Chinese and American beef marbling standards images. International Conference on Computer and Computing Technologies in Agriculture, (pp. 199–209). Zhangjiajie, China.
9. Serrano, S., & Perán, F., Jiménez-Hornero, F. J., & De Ravé, E. G. (2013). Multifractal analysis application to the characterization of fatty infiltration in Iberian and White pork sirloins. Meat science, 93(3), 723–732.
10. Avtanzimov, G. G. (1973). Morfometriya v patologi [Morphometry in Pathology]. Moscow  :  Meditsina [in Russian].
11. Koziy, M. S., & Ivanov, V. O. (2004). Sposib zaklyuchennya v parafin histolohichnykh ob’yektiv z fiksovanoyu tovshchynoyu – Method of entering in the paraffin wax of the histological objects with a fixed thickness : Рatent № 64288А [in Ukrainian].
12. Bianciardi, G., & Buonsanti, M., Pontari, A., & Tripodi, S. (2012). Fractal analysis and biophysical investigation of muscular tissue damaged due to low temperature: a pilot study. Journal of Biomimetics, Biomaterials and Tissue Engineering, 14, 43-51.
13. Hammer, Ř., & Harper, D. A., & Ryan, P. D. (2001). PAST: paleontological statistics software package for education and data analysis. Palaeontologiaelectronica, 4(1), 1-9.