Journals / Acta Infologica / 2019 / Cilt: 3 - Sayı: 2

The Application of Fractal Analysis on Thyroid Ultrasound Images

Tiroid Ultrason Görüntülerinde Fraktal Analiz Uygulaması

Pages
83–90
Publication Date
2022-09-04
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The complexity ofmedical 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 forphysicians in the treatment and early diagnosis of thyroid disorders

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The concept of fractals is used for complex and irregular objects or structures in systems.
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Key Findings & Data:The concept of fractals is used for complex and irregular objects or structures in systems.
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Conclusion & Impact:The complexity ofmedical 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 forphysicians in the treatment and early diagnosis of thyroid disorders
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

The concept of fractals is used for complex and irregular objects or structures in systems. The complexity ofmedical 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 forphysicians in the treatment and early diagnosis of thyroid disorders

Özet

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

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Cite This Article

BAYRAK, E. A., KIIRCI, P. (2022). The Application of Fractal Analysis on Thyroid Ultrasound Images. Acta Infologica, Cilt: 3 - Sayı: 2, 83–90. https://ajindex.com/articles/426005

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References (9)

  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 Fractalyse Analysis Software. (2018, November 05). Retrieved from http://www.fractalyse.org/
  4. Gonzato, G. (1998). A practical implementation of the box counting algorithm. Computers & Geosciences, 24(1), 95–100.
  5. 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 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.
  6. 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
  7. 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
  8. 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
  9. Macgillivray, T. J., Patton, N., Doubal, F. N., Graham, C., & Ward