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

Abstract

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.

Texture analysis of routine T2 weighted fat-saturated images can identify head and neck paragangliomas – A pilot study

“…The MRI signal in T2 images is relative, with variability between scanners and protocols affecting texture features obtained [ [34] , [35] , [36] ]. The cut-offs obtained from a single institute study on a single scanner may not be generalizable.…”

Texture analysis of cardiac cine magnetic resonance imaging to detect nonviable segments in patients with chronic myocardial infarction

“…We chose the mentioned pixel size to increment the heterogeneous information within the original images without introducing prejudicial noise . Subsequently, nonuniformity correction was applied throughout the myocardium ROI using an additive model . To minimize the influence of image brightness and contrast variation, normalization was implemented using a technique that remaps the ROI histogram to fit within the intensity mean ± 3 standard deviations …”

Editorial for “Investigation of the Inter‐ and Intra‐Scanner Reproducibility and Repeatability of Radiomics Features in Magnetic Resonance Imaging”

“…A way forward is to find a consensus on features that are reproducible, 6 which is where this study 5 contributes. However, one cannot be successful without a deeper understanding of the dependencies: the feature values depend both on parameter choices during calculations, 7 but also on the parameters of the MR pulse‐sequence 8,9 . Based on this understanding, we can define features that are robust with respect to the dependencies 10 .…”

“…However, one cannot be successful without a deeper understanding of the dependencies: the feature values depend both on parameter choices during calculations, 7 but also on the parameters of the MR pulse-sequence. 8,9 Based on this understanding, we can define features that are robust with respect to the dependencies. 10 To further facilitate the data-driven approach, although challenging, it is crucial to create large and curated databases that also include a large span of the underlying dependencies.…”