Dental and Medical Problems

Dent Med Probl
Index Copernicus (ICV 2020) – 128.41
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CiteScore (2021) – 2.0
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ISSN 1644-387X (print)
ISSN 2300-9020 (online)
Periodicity – quarterly

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Dental and Medical Problems

2017, vol. 54, nr 1, January-March, p. 79–83

doi: 10.17219/dmp/67501

Publication type: review article

Language: English

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Creative Commons BY-NC-ND 3.0 Open Access

The use of fractal analysis in medicine: A literature review

Zastosowanie analizy fraktalnej w medycynie – przegląd piśmiennictwa

Przemysław Leszczyński1,B,C,D,F, Jerzy Sokalski1,A,E,F

1 Department of Dental Surgery, Poznan University of Medical Sciences, Poland

Abstract

In many areas of science, there is a need to assess the complexity of the analyzed objects. One instrument used to assess this complexity can be fractal analysis, which provides a quantitative measure in the form of a fractal dimension. A fractal dimension is a quantitative parameter used for measuring the complexity of the examined objects. Fractal analysis is the process of information processing, where the input data is an image. The generated information is stored in the form of numbers, an array of numbers, the decision of the text etc. The implementation of the image processing requires a computer system. In the publications of recent years, there is increasing interest in the potential use of fractal analysis in many fields of science, including medicine. Fractal analysis expands the capabilities of the diagnosis of systemic diseases, facilitating and accelerating their examination. In dentistry, the calculation of the fractal dimension can be a tool for early detection of a periapical lesion on the basic X-rays. Fractal analysis can also be a helpful indicator in predicting the primary stability of an implant. On the basis of the literature and own experience, the authors sum up the use of fractal analysis in accordance with current medical knowledge.

Key words

fractal analysis, fractal dimension, fractals, dentistry

Słowa kluczowe

analiza fraktalna, wymiar fraklany, fraktale, stomatologia

References (30)

  1. Omiotek Z. The study of images self-similarity using the method of fractal analysis. Barometr regionalny. 2011;23:93–105 [in Polish].
  2. Kąkolewska J, Kuras M, Sokalski J, Kulczyk T. Use of fractal analysis for bone assessment. Dent Forum 2014;42:104–106.
  3. Garg A, Agrawal A, Negi A. A review on fractal image compression. Int J Comp Applicat. 2014;85:25–31.
  4. Sobolewska-Siemieniuk M, Grabowska S, Oczeretko E, Kitlas A, Borowska M. Fractal analysis of mandibular radiographic images in the region of reincluded teeth. Czas. Stomatol. 2007;60:593–600 [in Polish].
  5. Gawlik J, Magdziarczyk W, Wojnar L. Fractal analysis of geometric surface structure. Komputerowo Zintegrowane Zarządzanie 2011;2: 382–396 [in Polish].
  6. Lawrence MJ, Sabra A, Thomas P, et al. Fractal dimension: A novel clot microstructure biomarker use in ST elevation myocardial infarction patients. Atheroscler. 2015;240:402–407.
  7. Beckers F, Verheyden B, Couckuyt K, Aubert AE. Fractal dimension in health and heart failure. Biomed Tech. (Berl) 2006;51:194–197.
  8. Perkiömäki JS, Mäkikallio TH, Huikuri HV. Fractal and complexity measures of heart rate variability. Clin Exp Hypertens. 2005;27:149–158.
  9. Tapanainen JM, Thomsen PE, Kober L, et al. Fractal analysis of heart rate variability and mortality after an acute myocardial infarction. Am J Cardiol. 2002;90:347–352.
  10. Waliszewski P, Wagenlehner F, Gattenlöhner S, Weidner W. Fractal geometry in the objective grading of prostate carcinoma. Urologe A. 2014;53:1186–1194.
  11. Daye D, Keller B, Conant EF, Chen J, Schnall MD, Maidment AD, Kontos D. Mammographic parenchymal patterns as an imaging marker of endogenous hormonal exposure: A preliminary study in a high-risk population. Acad Radiol. 2013;20:635–646.
  12. Zheng Y, Keller BM, Ray S, Wang Y, Conant EF, Gee JC, Kontos D. Parenchymal texture analysis in digital mammography: A fully automated pipeline for breast cancer risk assessment. Med Phys. 2015;42:4149–4160.
  13. Heymans O, Blacher S, Brouers F, Piérard GE. Fractal quantification of the microvasculature heterogeneity in cutaneous melanoma. Derma-tol. 1999;198:212–217.
  14. Gheonea DI, Streba CT, Vere CC, et al. Diagnosis system for hepatocellular carcinoma based on fractal dimension of morphometric elements integrated in an artificial neural network. Biomed Res Int. 2014;239706.
  15. Frydkjaer-Olsen U, Soegaard Hansen R, Pedersen K, Peto T, Grauslund J. Retinal vascular fractals correlate with early neurodegeneration in patients with type 2 diabetes mellitus. Invest Ophthalmol Vis Sci. 2015;56:7438–7443.
  16. Thomas GN, Ong SY, Tham YC, et al. Measurement of macular fractal dimension using a computer-assisted program. Invest Ophthalmol Vis Sci. 2014;55:2237–2243.
  17. Tălu S. Multifractal characterisation of human retinal blood vessels. Oftalmol. 2012;56:63–71.
  18. Jiang H, Debuc DC, Rundek T, et al. Automated segmentation and fractal analysis of high-resolution non-invasive capillary perfusion maps of the human retina. Microvasc Res. 2013;89:172–175.
  19. White SC. Oral radiographic predictors of osteoporosis. Dentomaxillofac Radiol. 2002;31:84–92.
  20. Sindeaux R, Figueiredo PT, de Melo NS, et al. Fractal dimension and mandibular cortical width in normal and osteoporotic men and women. Maturitas 2014;77:142–148.
  21. Bianciardi G, Bisogno S, Bertoldi I, Laurini L, Coviello G, Frediani B. Fractal dimension of bone texture in radiographs correlates to ultrasound broadband attenuation T-score. Clin Exp Rheumatol. 2013;31:389–393.
  22. Udhayakumar G, Sujatha CM, Ramakrishnan S. Trabecular architecture analysis in femur radiographic images using fractals. Proc Inst Mech Eng H. 2013;227:448–453.
  23. Luo G, Kinney JH, Kaufman JJ, Haupt D, Chiabrera A, Siffert RS. Relationship between plain radiographic patterns and three dimensional trabecular architecture in the human calcaneus. Osteoporos Int. 1999;9:339–345.
  24. Linkow LI. Some variant designs of the subperiosteal implant. Oral Implantol. 1972;2:190–205.
  25. Soğur E, Baksı BG, Gröndahl HG, Sen BH. Pixel intensity and fractal dimension of periapical lesions visually indiscernible in radiographs. J Endod. 2013;39:16–19.
  26. Huang CC, Chen JC, Chang YC, Jeng JH, Chen CM. A fractal dimensional approach to successful evaluation of apical healing. Int Endod J. 2013;46:523–529.
  27. Torres SR, Chen CS, Leroux BG, Lee PP, Hollender LG, Schubert MM. Fractal dimension evaluation of cone beam computed tomography in patients with bisphosphonate-associated osteonecrosis. Dentomaxillofac Radiol. 2011;40:501–505.
  28. Kąkolewska J. Evaluation of the alveolar bone using fractal analysis and periotest device in patients after implantation of dental implants. Doctoral Thesis, Poznań 2014 [in Polish].
  29. Kozakiewicz M, Chaberek S, Bogusiak K. Using fractal dimension to evaluate alveolar bone defects treated with various bone substitute materials. Central Europ J Med. 2013;8:776–789.
  30. Baksi BG, Fidler A. Image resolution and exposure time of digital radiographs affects fractal dimension of periapical bone. Clin Oral Investig. 2012;16:1507–1510.