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Dergiler / Celal Bayar Üniversitesi Fen Bilimleri Dergisi / 2018 / Cilt: 14 - Sayı: 1

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

Evgin GÖÇERİ
Sayfa
125–134
Yayın Tarihi
2022-10-12
DOI
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Comparisons of all methods implemented in this work indicate a better performance of the proposed approach for brain magnetic resonance images.

📌 3 Maddede Bu Makale

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Amaç & Hipotez: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.
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Temel Bulgular & Veri:In addition to intensity variations, low contrast and partial volume effects increases the difficulty in automated methods with these images.
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Sonuç & Katkı:Comparisons of all methods implemented in this work indicate a better performance of the proposed approach for brain magnetic resonance images.
🔬Tür:Literatür & Derleme Çalışması
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Alan Parametreleri

Abstract

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.

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Bu Makaleye Atıf Yap

GÖÇERİ, E. (2022). Fully Automated and Adaptive Intensity Normalization Using Statistical Features for Brain MR Images. Celal Bayar Üniversitesi Fen Bilimleri Dergisi, Cilt: 14 - Sayı: 1, 125–134. https://ajindex.com/articles/472272

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  1. University of Southern California, Brain Image Database. http://loni.usc.edu 2009 (accessed 25.10.2017). 47. Frandsen, P.B, Calcott, B, Mayer, C, Lanfear, R, Automatic selection of partitioning schemes for phylogenetic analyses using iterative k- means clustering of site rates. BMC Evolutionary Biology, 2015, 15(1), 13-39. 46. Sarrafzadeh, O, Dehnavi, A.M, Nucleus and cytoplasm segmentation in microscopic images using K-means clustering and region growing. Journal of Advanced Biomedical Research, 2015, 4(1), 174-184. 45. Ayech, M,W, Ziou, D, Terahertz image segmentation using k-means clustering based on weighted feature learning and random pixel sampling, Neurocomputing, 2016, 175(Part A), 243-264. 44. Amorim, R.C, Makarenkov, V, Applying subclustering and Lp distance in weighted k-means with distributed centroids. Neurocomputing, 2016, 173(3), 700-707. 43. Dempster, A.P, Laird, N.M, Donald, B, Maximum likelihood from incomplete data via the EM algorithm, Paper presented at the Royal Statistical Society at a meeting organized by the Research Section, Atlantic City, N.J, 1977, pp.1–38. 42. Bishop, C.M, Mixture models and EM. In: Jordan, M, Kleinberg, J, Schölkopf, B, (editors) Pattern recognition and machine learning. Springer, New York, 2006, pp.423-455. 41. Goceri, E, Goksel, B, Elder, J. B, Puduvalli, V. K, Otero, J. J, Gurcan, M. N, Quantitative validation of anti‐PTBP1 antibody for diagnostic neuropathology use: Image analysis approach, International Journal of Numerical Methods in Biomedical Engineering, 2017, e2862, 1-14. 40. Feng, M.L, Tan, Y.P, Contrast adaptive binarization of low quality document images, IEICE Electron Express, 2004, 1(16), 501-506. 39. Waarde, J. A, Scholte, H. S, Oudheusden, L. J. B, Verwey, B, Denys, D, Wingen, G.A, A functional MRI marker may predict the outcome of electroconvulsive therapy in severe and treatment-resistant depression, Molecular Psychiatry, 2015, 20, 609–614. 38. Liu, L.X, Zhang, Q, Wu, M, Li, W, Shang, F, Adaptive segmentation of magnetic resonance images with intensity inhomogeneity using level set method, Magnetic Resonance Imaging, 2013, 31(4), 567– 574. 37. Zhang, K.H, Zhang, L, Lam, K.M, Zhang, D, A local active contour model for image segmentation with intensity inhomogeneity, 2013, arXiv:1305.7053, 1-27. 36. Illán, I.A, Górriz, J.M, Ramírez, J, Salas-González, D, López, M.M, Segovia, F, Chaves, R, Gómez-Rio, M, Puntonet, C.G, 18F-FDG PET imaging analysis for computer aided Alzheimer's diagnosis, I