Yıl 2018, Cilt 14, Sayı 1, Sayfalar 125 - 134 2018-03-30

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

Evgin Goceri [1]

45 63

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.

Brain MR images, Gaussian filtering, Homomorphic filtering, Intensity normalization, Local normalization
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Yazar: Evgin Goceri (Sorumlu Yazar)
E-posta: evgingoceri@yahoo.com
Ülke: Turkey


Bibtex @araştırma makalesi { cbayarfbe384729, journal = {Celal Bayar Üniversitesi Fen Bilimleri Dergisi}, issn = {1305-130X}, address = {Celal Bayar Üniversitesi}, year = {2018}, volume = {14}, pages = {125 - 134}, doi = {10.18466/cbayarfbe.384729}, title = {Fully Automated and Adaptive Intensity Normalization Using Statistical Features for Brain MR Images}, key = {cite}, author = {Goceri, Evgin} }
APA Goceri, E . (2018). Fully Automated and Adaptive Intensity Normalization Using Statistical Features for Brain MR Images. Celal Bayar Üniversitesi Fen Bilimleri Dergisi, 14 (1), 125-134. DOI: 10.18466/cbayarfbe.384729
MLA Goceri, E . "Fully Automated and Adaptive Intensity Normalization Using Statistical Features for Brain MR Images". Celal Bayar Üniversitesi Fen Bilimleri Dergisi 14 (2018): 125-134 <http://dergipark.gov.tr/cbayarfbe/issue/36240/384729>
Chicago Goceri, E . "Fully Automated and Adaptive Intensity Normalization Using Statistical Features for Brain MR Images". Celal Bayar Üniversitesi Fen Bilimleri Dergisi 14 (2018): 125-134
RIS TY - JOUR T1 - Fully Automated and Adaptive Intensity Normalization Using Statistical Features for Brain MR Images AU - Evgin Goceri Y1 - 2018 PY - 2018 N1 - doi: 10.18466/cbayarfbe.384729 DO - 10.18466/cbayarfbe.384729 T2 - Celal Bayar Üniversitesi Fen Bilimleri Dergisi JF - Journal JO - JOR SP - 125 EP - 134 VL - 14 IS - 1 SN - 1305-130X-1305-1385 M3 - doi: 10.18466/cbayarfbe.384729 UR - http://dx.doi.org/10.18466/cbayarfbe.384729 Y2 - 2018 ER -
EndNote %0 Celal Bayar Üniversitesi Fen Bilimleri Dergisi Fully Automated and Adaptive Intensity Normalization Using Statistical Features for Brain MR Images %A Evgin Goceri %T Fully Automated and Adaptive Intensity Normalization Using Statistical Features for Brain MR Images %D 2018 %J Celal Bayar Üniversitesi Fen Bilimleri Dergisi %P 1305-130X-1305-1385 %V 14 %N 1 %R doi: 10.18466/cbayarfbe.384729 %U 10.18466/cbayarfbe.384729