Morphological Exponential Entropy Driven-HUM

Ardizzone E; Pirrone R; Gambino O

Risultato della ricerca: Paper

1 Citazione (Scopus)

Abstract

This paper presents an improvement to the exponential entropy driven-homomorphic unsharp masking (E2D-HUM) algorithm devoted to illumination artifact suppression on magnetic resonance images. E2D-HUM requires a segmentation step to remove dark regions in the foreground whose intensity is comparable with background, because strong edges produce streak artifacts on the tissues. This new version of the algorithm keeps the same good properties of E2D-HUM without a segmentation phase, whose parameters should be chosen in relation to the image
Lingua originaleEnglish
Stato di pubblicazionePublished - 2006

Fingerprint

Entropy
Artifacts
Magnetic resonance
Lighting
Magnetic Resonance Spectroscopy
Tissue

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Health Informatics
  • Computer Vision and Pattern Recognition
  • Biomedical Engineering

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Ardizzone E; Pirrone R; Gambino O (2006). Morphological Exponential Entropy Driven-HUM.

Morphological Exponential Entropy Driven-HUM. / Ardizzone E; Pirrone R; Gambino O.

2006.

Risultato della ricerca: Paper

Ardizzone E; Pirrone R; Gambino O 2006, 'Morphological Exponential Entropy Driven-HUM'.
Ardizzone E; Pirrone R; Gambino O. Morphological Exponential Entropy Driven-HUM. 2006.
Ardizzone E; Pirrone R; Gambino O. / Morphological Exponential Entropy Driven-HUM.
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abstract = "This paper presents an improvement to the exponential entropy driven-homomorphic unsharp masking (E2D-HUM) algorithm devoted to illumination artifact suppression on magnetic resonance images. E2D-HUM requires a segmentation step to remove dark regions in the foreground whose intensity is comparable with background, because strong edges produce streak artifacts on the tissues. This new version of the algorithm keeps the same good properties of E2D-HUM without a segmentation phase, whose parameters should be chosen in relation to the image",
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AB - This paper presents an improvement to the exponential entropy driven-homomorphic unsharp masking (E2D-HUM) algorithm devoted to illumination artifact suppression on magnetic resonance images. E2D-HUM requires a segmentation step to remove dark regions in the foreground whose intensity is comparable with background, because strong edges produce streak artifacts on the tissues. This new version of the algorithm keeps the same good properties of E2D-HUM without a segmentation phase, whose parameters should be chosen in relation to the image

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