Tiroid Ultrason Görüntülerinde Fraktal Analiz Uygulaması

Abstract

Fraktal kavramı, sistemlerdeki karmaşık ve düzensiz nesneler veya yapılar için kullanılmaktadır. Medikal görüntülerin karmaşıklığı, görüntülerin karakterizasyonu için Öklid geometrisi yerine Fraktal geometrisini kullanmamıza izin verir. Bu çalışmada, tiroid ultrason görüntüleri fraktal boyutu hesaplamak için analiz edilmiştir. Fraktal analiz yazılımı, korelasyon katsayısının ve fraktal boyut değerlerinin hesaplanması için kullanılmıştır. Hesaplanan fraktal boyut değerleri tiroid ultrason görüntü verisine göre değişmektedir. Tiroid ultrason görüntülerinde fraktal analiz ile bölgede yer alan kistlerin, nodüllerin veya lezyonların fraktal kavramı kullanılarak boyutlarının hesaplanmasının, tiroid hastalıklarının erken teşhis edilmesinde ve tedavisinde hekimlere yardımcı sistem olarak kullanılabileceği öngörülmektedir.

Keywords

• fraktal analiz,tiroid ultrason görüntüsü,kutu sayma yöntemi

The Application of Fractal Analysis on Thyroid Ultrasound Images

Abstract

The concept of fractals is used for complex and irregular objects or structures in systems. The complexity of medical images allows us to use Fractal geometry rather then Euclidian geometry for their characterization. In this paper, we have discussed the analysis of thyroid ultrasound images to calculate fractal dimension. Fractal analysis software was used for the calculation of fractal dimension and correlation coefficient values. In the fractal analysis on the thyroid ultrasound images, the determination of the dimensions of the lesions, nodules or cysts in the region by using fractal concepts can be predicted and used as an assistive system for physicians in the treatment and early diagnosis of thyroid disorders.

Keywords

• Fractal Analysis,Box Counting Method,Fractal Dimension

References

1. Acharya, U. R., Swapna, G., Sree, S. V., Molinari, F., Gupta, S., Bardales, R. H.... Suri, J. S. (2014). A review on ultrasound-based thyroid cancer tissue characterization and automated classification. Technology in Cancer Research & Treatment, 13(4), 289–301. https://doi.org/10.7785/tcrt.2012.500381
2. Ashamallah, G. A., & EL-Adalany, M. A. (2016). Risk for malignancy of thyroid nodules: Comparative study between TIRADS and US based classification system. The Egyptian Journal of Radiology and Nuclear Medicine, 47(4), 1373–1384. https://doi.org/10.1016/j.ejrnm.2016.08.021
3. Chakravarty, S. P., & Chakraborty, S. (2017, December). Fractal analysis related to tumour growth in lungs: A review. In 2017 Fourth International Conference on Image Information Processing (ICIIP) (pp. 1–4). IEEE. https://doi.org/10.1109/ICIIP.2017.8313752
4. Fractalyse Analysis Software. (2018, November 05). Retrieved from http://www.fractalyse.org/
5. Gonzato, G. (1998). A practical implementation of the box counting algorithm. Computers & Geosciences, 24(1), 95–100.
6. Hadzieva, E., Bogatinoska, D. C., Petroski, R., Shuminoska, M., Gjergjeska, L., Karadimce, A., & Trajkova, V. (2016). Is the fractal dimension of the contour-lines a reliable tool for classification of medical images? In MATEC Web of Conferences (Vol. 76, p. 05002). EDP Sciences. https://doi. org/10.1051/matecconf/20167605002
7. Hadzieva, E., Bogatinoska D. C., Shuminoska M., Petroski, R. (2017). On the reliability of the fractal dimension as a characteristic of the medical images contours. WSEAS Transactions on Biology and Biomedicine, 14, 19–28.
8. Lennon, F. E., Cianci, G. C., Cipriani, N. A., Hensing, T. A., Zhang, H. J., Chen, C. T. ... Salgia, R. (2015). Lung cancer a fractal viewpoint. Nature reviews Clinical oncology, 12(11), 664. https://dx.doi.org/10.1038%2Fnrclinonc.2015.108

9. Lupo, M., Leguto, A. J., Bortolato, S. A., & Korol, A. M. (2016). Evolution of erythrocytes aggregation: A fractal approach when incubated with Trichinella spiralis and Ascaris lumbricoides. Ain Shams Engineering Journal, 9(4), 1621–1625. https://doi.org/10.1016/j.asej.2016.12.004
10. Lv, D., Guo, X., Wang, X., Zhang, J., & Fang, J. (2009). Computerized characterization of prostate cancer by fractal analysis in MR images. Journal of Magnetic Resonance Imaging: An Official Journal of the International Society for Magnetic Resonance in Medicine, 30(1), 161–168. https://doi.org/10.1002/jmri.21819
11. Macgillivray, T. J., Patton, N., Doubal, F. N., Graham, C., & Wardlaw, J. M. (2007, August). Fractal analysis of the retinal vascular network in fundus images. In 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, (pp. 6455–6458). IEEE. 10.1109/ IEMBS.2007.4353837
12. Mandelbrot, B. B. (1967). How long is the coast of Britain? Statistical self-similarity and fractional dimension. Science, 156(3775), 636–638. https://doi.org/10.1126/science.156.3775.636
13. Mandelbrot, B. B. (1983). The fractal geometry of nature. New York, NY: W.H. Freeman and Company.
14. Pedraza, L., Vargas, C., Narváez, F., Durán, O., Muñoz, E., & Romero, E. (2015, January). An open access thyroid ultrasound image database. In 10th International Symposium on Medical Information Processing and Analysis (Vol. 9287, p. 92870W). International Society for Optics and Photonics.
15. Savelonas, M., Maroulis, D., & Sangriotis, M. (2009). A computer-aided system for malignancy risk assessment of nodules in thyroid US images based on boundary features. Computer Methods and Programs in Biomedicine, 96(1), 25–32. https://doi.org/10.1016/j.cmpb.2009.04.001
16. THEMA. . (2018, November 05). Retrieved from http://thema.univ-fcomte.fr/en/
17. Thyroid Digital Image Database. (2018, December 12). Retrieved from http://cimalab.intec.co/?lang=en&mod=program&id=5
18. Timbo, C., da Rosa, L. A. R., Goncalves, M., & Duarte, S. B. (2009). Computational cancer cells identification by fractal dimension analysis. Computer Physics Communications, 180(6), 850–853. https://doi.org/10.1016/j.cpc.2008.12.011
19. Uahabi, K. L., & Atounti, M. (2015). Applications of fractals in medicine. Annals of the University of Craiova-Mathematics and Computer Science Series, 42(1), 167–174.
20. Voinea, V., & Popescu, D. (2011). Fractal analysis in electrography for biological systems diagnosing. UPB Sci Bull, 73, 29–42.