Differential diagnosis of CT focal liver lesions using texture features, feature selection and ensemble driven classifiers

@article{Mougiakakou2007DifferentialDO,
  title={Differential diagnosis of CT focal liver lesions using texture features, feature selection and ensemble driven classifiers},
  author={Stavroula G. Mougiakakou and Ioannis K. Valavanis and Alexandra Nikita and Konstantina S. Nikita},
  journal={Artificial intelligence in medicine},
  year={2007},
  volume={41 1},
  pages={
          25-37
        }
}
OBJECTIVES The aim of the present study is to define an optimally performing computer-aided diagnosis (CAD) architecture for the classification of liver tissue from non-enhanced computed tomography (CT) images into normal liver (C1), hepatic cyst (C2), hemangioma (C3), and hepatocellular carcinoma (C4). To this end, various CAD architectures, based on texture features and ensembles of classifiers (ECs), are comparatively assessed. MATERIALS AND METHODS Number of regions of interests (ROIs… CONTINUE READING
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References

Publications referenced by this paper.
Showing 1-10 of 40 references

Rapid texture identification

  • KI Laws
  • 1980
Highly Influential
16 Excerpts

A novel approach to diagnose diabetes based on the fractal characteristics of retinal images

  • Cheng S-Ch, Huang Y-M
  • IEEE Trans Inf Technol Biomed
  • 2003
1 Excerpt