title: Autocorrelation method for fractal analysis in nonrectangular image domains.
abstract: We report an autocorrelation-based approach that accurately measures fractal organization within arbitrarily shaped (nonrectangular) regions of interest of gray-scale images. It extends fractal analysis beyond what is possible using fast Fourier transforms and improves on a previous autocorrelation algorithm. We illustrate its use in detecting subtle changes in mitochondrial organization within murine fibroblasts expressing the human papillomavirus E7 oncogene.
authors.forename: Douglas H
authors.initials: DH
authors.lastname: MacDonald
authors.forename: Martin
authors.initials: M
authors.lastname: Hunter
authors.forename: Kyle P
authors.initials: KP
authors.lastname: Quinn
authors.forename: Irene
authors.initials: I
authors.lastname: Georgakoudi
mesh_terms: D000465:Algorithms; D000818:Animals; D005347:Fibroblasts / Q000166:cytology / Q000378:metabolism; D017709:Fractals; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D051379:Mice; D061848:Optical Imaging / Q000379:methods*; D050725:Papillomavirus E7 Proteins / Q000235:genetics
chemical_list: D050725:Papillomavirus E7 Proteins; C059731:oncogene protein E7, Human papillomavirus type 16