A Fractal Analysis of CT Liver Images for the Discrimination of Hepatic Lesions: A Comparative Study

Abstract

A quantitative study for the discrimination of different hepatic lesions is presented in this paper. The study is based on the fractal analysis of CT liver images in order to estimate their fractal dimension and to differentiate normal liver parenchyma from hepatocellular carcinoma. Four fractal dimension estimators have been implemented throughout this work; three well-established methods and a novel implementation of a method. Analytically, these methods correspond to the power spectrum method, the box counting method, the morphological fractal estimator and the novel modification of the kth-nearest neighbor method. The Fuzzy C-Means algorithm is finally applied revealing that the k-th nearest neighbor method outperforms the other methods; thus discriminating up to 93% of the normal parenchyma and up to 82% of the hepatocellular carcinoma, correctly.

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Document Details

Document Type
Technical Report
Publication Date
Oct 25, 2001
Accession Number
ADA412488

Entities

People

  • A. S. Nikita
  • C. P. Sariyanni
  • G. K. Matsopoulos
  • K. S. Nikita
  • P. Asvestas

Tags

Communities of Interest

  • Air Platforms
  • Biomedical
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Acquisition
  • Algorithms
  • Classification
  • Counting Methods
  • Data Acquisition
  • Diagnostic Imaging
  • Discriminant Analysis
  • Discriminate Analysis
  • Discrimination
  • Estimators
  • Health Services
  • Image Processing
  • Local Area Networks
  • Magnetic Resonance
  • Power Spectra
  • Spectra
  • X-Ray Computed Tomography

Readers

  • Computer Vision.
  • Statistical inference.
  • Toxicology/Environmental Toxicology