Fully Automated and Adaptive Intensity Normalization Using Statistical Features for Brain MR Images

Yıl 2018, , 125 - 134, 30.03.2018

Evgin Goceri

https://doi.org/10.18466/cbayarfbe.384729

Cited By: 26

Öz

Accuracy of the results obtained by automated processing of brain magnetic
resonance images has vital importance for diagnosis and evaluation
of a progressive disease during treatment. However,
automated processing methods such as segmentation, registration and comparison
of these images are challenging issues. Because intensity values do not only
depend on the underlying tissue type. They can change due to scanner-related
artifacts and noise, which usually occurs in magnetic resonance images. In
addition to intensity variations, low contrast and partial volume effects
increases the difficulty in automated methods with these images. Intensity
normalization has a significant role to increase performance of automated image
processing methods. Because it is applied as a pre-processing step and efficiency
of the other steps in these methods is based on the results obtained from the
pre-processing step. The goal of intensity normalization is to make uniform the
mean and variance values in images. Different methods have been applied for
this purpose in the literature and each method has been tested with different
kind of images. In this work; 1) The state-of-art normalization methods applied
for magnetic resonance images have been reviewed. 2) A fully automated and
adaptive approach has been proposed for intensity normalization in brain
magnetic resonance images. 3) Comparative performance evaluations of the
results obtained by four different normalization approaches using the same
images have been presented. Comparisons of all methods implemented in this work
indicate a better performance of the proposed approach for brain magnetic
resonance images.

Anahtar Kelimeler

Brain MR images, Gaussian filtering, Homomorphic filtering, Intensity normalization, Local normalization

Kaynakça

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