<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Computerized texture analysis characterizes spatial patterns of image intensity, which originate in the structure of tissues. However, a number of texture descriptors also depend on local average image intensity and/or contrast. This variations, known as image nonuniformity (inhomogeneity) artefacts often occur, e.g. in MRI. Their presence may lead to errors in tissue description. This unwanted effect is explained in this paper using statistical texture descriptors applied for MRI slices of a normal and fibrotic liver. To reduce the errors, correction of image spatial nonuniformity prior to texture analysis is performed. The issue of sensitivity of popular texture parameters to image nonuniformities is discussed. It is illustrated by classification examples of natural Brodatz textures, digitally modified to account for inhomogeneities – modeled as smooth variations of image intensity and contrast. A set of texture features is identified which represent certain immunity to image inhomogeneities.</p></abstract>

Computerized texture analysis characterizes spatial patterns of image intensity, which originate in the structure of tissues. However, a number of texture descriptors also depend on local average image intensity and/or contrast. This variations, known as image nonuniformity (inhomogeneity) artefacts often occur, e.g. in MRI. Their presence may lead to errors in tissue description. This unwanted effect is explained in this paper using statistical texture descriptors applied for MRI slices of a normal and fibrotic liver. To reduce the errors, correction of image spatial nonuniformity prior to texture analysis is performed. The issue of sensitivity of popular texture parameters to image nonuniformities is discussed. It is illustrated by classification examples of natural Brodatz textures, digitally modified to account for inhomogeneities – modeled as smooth variations of image intensity and contrast. A set of texture features is identified which represent certain immunity to image inhomogeneities.

On The Effect Of Image Brightness And Contrast Nonuniformity On Statistical Texture Parameters

Institute of Electronics, Lodz University of Technology, Wolczanska 211/215, 90-924 Lodz, Poland

Foundations of Computing and Decision Sciences

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Computerized texture analysis characterizes spatial patterns of image intensity, which originate in the structure of tissues. However, a number of texture...

On The Effect Of Image Brightness And Contrast Nonuniformity On Statistical Texture Parameters

Online veröffentlicht: 30. Sept. 2015

Seitenbereich: 163 - 185

DOI: https://doi.org/10.1515/fcds-2015-0011

Schlüsselwörter
texture analysis, magnetic resonance imaging, image normalization, gray level nonuniformity correction, texture features sensitivity

© Andrzej Materka et al.

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

On The Effect Of Image Brightness And Contrast Nonuniformity On Statistical Texture Parameters

Online veröffentlicht: 30. Sept. 2015

Seitenbereich: 163 - 185

DOI: https://doi.org/10.1515/fcds-2015-0011

Schlüsselwörtertexture analysis, magnetic resonance imaging, image normalization, gray level nonuniformity correction, texture features sensitivity

© Andrzej Materka et al.

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
texture analysis, magnetic resonance imaging, image normalization, gray level nonuniformity correction, texture features sensitivity