title: MRI Image Processing Based on Fractal Analysis
abstract: Background: Cancer is one of the most common causes of human mortality, with about 14 million new cases and
  8.2 million deaths reported in in 2012. Early diagnosis of cancer through screening allows interventions to reduce
  mortality. Fractal analysis of medical images may be useful for this purpose. Materials and Methods: In this study,
  we examined magnetic resonance (MR) images of healthy livers and livers containing metastases from colorectal
  cancer. The fractal dimension and the Hurst exponent were chosen as diagnostic features for tomographic imaging using
  Image J software package for image processings FracLac for applied for fractal analysis with a 120x150 pixel area.
  Calculations of the fractal dimensions of pathological and healthy tissue samples were performed using the box-counting
  method. Results: In pathological cases (foci formation), the Hurst exponent was less than 0.5 (the region of unstable
  statistical characteristics). For healthy tissue, the Hurst index is greater than 0.5 (the zone of stable characteristics).
  Conclusions: The study indicated the possibility of employing fractal rapid analysis for the detection of focal lesions
  of the liver. The Hurst exponent can be used as an important diagnostic characteristic for analysis of medical images.
authors.affiliation: ITMO University, Saint Petersburg, Russia. Email: satikov.vi@gmail.com
authors.forename: Mariya Y
authors.initials: MY
authors.lastname: Marusina
authors.forename: Alexandra P
authors.initials: AP
authors.lastname: Mochalina
authors.forename: Ekaterina P
authors.initials: EP
authors.lastname: Frolova
authors.forename: Valentin I
authors.initials: VI
authors.lastname: Satikov
authors.forename: Anton A
authors.initials: AA
authors.lastname: Barchuk
authors.forename: Vladimir I
authors.initials: VI
authors.lastname: Kuznetcov
authors.forename: Vadim S
authors.initials: VS
authors.lastname: Gaidukov
authors.forename: Segrey A
authors.initials: SA
authors.lastname: Tarakanov
keywords: Computer aided diagnosis; cancer; fractal analysis; data set; screening; machine learning