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Diagnosis of Alzheimer's Disease Using Atlas-Based Volume Measurement Method on 3D T1 Weighted MR Images

Yıl 2022, Cilt: 25 Sayı: 1, 47 - 58, 01.03.2022
https://doi.org/10.2339/politeknik.728199

Öz

Alzheimer's Disease is a brain disease that begins with aging. Diagnosis of the disease, its follow-up and measurements of the related brain regions can be performed with high-resolution three-dimensional structural magnetic resonance images. In this study, an atlas-based volume measurement and classification model were designed that can perform volumetric measurement of 116 subcortical regions on 70 Alzheimer 70 Normal 3D T1-weighted MR images taken from the OASIS database. The measured volume values were normalized by dividing gray matter, parenchyma, and total brain volume in each subject. Thus, 4 different datasets with 140x116 matrix size, including raw measured values, were obtained. Datasets were ranked from the most meaningful feature to the most meaningless feature with entropy, t-test, roc, Bhattacharyya, Wilcoxon feature ranking methods. The ranked data were combined in each cycle, respectively, and the classification process was performed by giving linear and rbf kernel support vector machines with 10-fold cross validations. Data cluster, feature ranking method and classification method that give the best results with the least feature were determined by analyzing all scenario. The effect of normalization and feature ranking methods on the classification results were examined. As a result of experimental operations, the roc feature ranking based linear support vector machine gives the highest rates with 95.71% sensitivity, 94.29% specificity, 95.00% accuracy, 0.95 area under curve values using 107 features with total brain volume normalization

Kaynakça

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  • [2] Türkiye Alzheimer Derneği, "Türkiye'de 600 bin aile Alzheimer Hastalığı ile Mücadele Ediyor", http://www. alzheimerdernegi.org.tr/haber/turkiyede-600-bin-aile- alzheimer-hastaligi-ile-mucadele-ediyor/ (10.02.2020).
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  • [4] Gürvit H., Baran B., "Demanslar ve Kognitif Bozukluklarda Ölçekler", Nöropsikiyatri Arşivi, 44: 58-65, (2007).
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  • [7] Öztürk Ş., Akdemir B., "HIC-net: A deep convolutional neural network model for classification of histopathological breast images,"Computers & Electrical Engineering, 76: 299-310, (2019).
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  • [9] Öztürk G.B., Karan M.A., "Alzheimer Hastalığının Fizyopatolojisi", Klinik Gelişim Dergisi, 36-45, (2006).
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  • [27] Öziç M.Ü., Ekmekci A.H., Özşen S., "Atlas-Based Segmentation Pipelines on 3D Brain MR Images: A Preliminary Study", BRAIN. Broad Research in Artificial Intelligence Neuroscience, 9(4):129-140, (2018).
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  • [36] Killiany R.J., Moss M.B., Albert M.S., Sandor T., Tieman J., Jolesz F.,"Temporal lobe regions on magnetic resonance imaging identify patients with early Alzheimer's disease", Archives of Neurology, 50(9): 949-954, (1993).
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3B T1 Ağırlıklı MR Görüntülerinde Atlas Tabanlı Hacim Ölçüm Yöntemini Kullanarak Alzheimer Hastalığının Teşhisi

Yıl 2022, Cilt: 25 Sayı: 1, 47 - 58, 01.03.2022
https://doi.org/10.2339/politeknik.728199

Öz

Alzheimer Hastalığı yaşlılık ile beraber başlayan bir beyin hastalığıdır. Hastalığın teşhisi, takibi ve ilgili beyin bölgelerinin ölçümleri yüksek çözünürlüklü üç boyutlu yapısal manyetik rezonans görüntüleri ile yapılabilmektedir. Bu çalışmada, OASIS veri tabanından alınan 70 Alzheimer 70 Normal 3B T1 ağırlıklı MR görüntüleri üzerinde 116 subkortikal bölgenin hacimsel ölçümünü yapabilecek atlas tabanlı bir hacim ölçüm ve sınıflandırma modeli tasarlanmıştır. Ölçülen değerler her bir denekte gri madde, parankim, total beyin hacmi ile bölünerek normalizasyon işlemi yapılmıştır. Böylece ham ölçülen değerler dahil olmak üzere 140x116 matris boyutlu 4 farklı veri kümesi elde edilmiştir. Veri kümeleri entropi, t-test, roc, Bhattacharyya, Wilcoxon özellik derecelendirme yöntemleri ile en anlamlı özellikten en anlamsız özelliğe doğru derecelendirilmiştir. Derecelendirilen veriler her döngüde sırasıyla birleştirilmiş, lineer ve rbf kernel kullanan destek vektör makinelerine 10-kat çapraz geçerleme ile verilerek sınıflandırma işlemi yapılmıştır. Tüm senaryolar analiz edilerek, en az özellikle en iyi sonucu veren küme, özellik derecelendirme ve sınıflandırma metodu ortaya konulmuştur. Normalizasyon ve özellik derecelendirme yöntemlerinin sınıflandırma sonucuna etkisi incelenmiştir. Deneysel işlemler sonucunda roc özellik derecelendirme tabanlı lineer destek vektör makinesi, total beyin hacmi normalizasyonlu 107 özellik kullanarak %95.71 hassasiyet, %94.29 özgüllük, %95.00 doğruluk, 0.95 eğri altında kalan alan değerleri ile en yüksek oranları vermektedir.

Kaynakça

  • [1] Selekler K., "Alois Alzheimer ve Alzheimer Hastalığı", Türk Geriatri Dergisi, 13: 9-14, (2010).
  • [2] Türkiye Alzheimer Derneği, "Türkiye'de 600 bin aile Alzheimer Hastalığı ile Mücadele Ediyor", http://www. alzheimerdernegi.org.tr/haber/turkiyede-600-bin-aile- alzheimer-hastaligi-ile-mucadele-ediyor/ (10.02.2020).
  • [3] Association A., "2019 Alzheimer's disease facts and figures", Alzheimer's & Dementia, 15(3):321-387, (2019).
  • [4] Gürvit H., Baran B., "Demanslar ve Kognitif Bozukluklarda Ölçekler", Nöropsikiyatri Arşivi, 44: 58-65, (2007).
  • [5] Lazli L., Boukadoum M., Mohamed O.A., "A Survey on Computer-Aided Diagnosis of Brain Disorders through MRI Based on Machine Learning and Data Mining Methodologies with an Emphasis on Alzheimer Disease Diagnosis and the Contribution of the Multimodal Fusion", Applied Sciences, 10(5):1894, (2020).
  • [6] Liu L., Zhao S., Chen H., Wang A., "A new machine learning method for identifying Alzheimer's disease", Simulation Modelling Practice Theory, 99:102023, (2020).
  • [7] Öztürk Ş., Akdemir B., "HIC-net: A deep convolutional neural network model for classification of histopathological breast images,"Computers & Electrical Engineering, 76: 299-310, (2019).
  • [8] Sezer C., Memiş L.,"Alzheimer Hastalığı Histopatolojisi” , Demans Dergisi, 1(2):42-49, (2001).
  • [9] Öztürk G.B., Karan M.A., "Alzheimer Hastalığının Fizyopatolojisi", Klinik Gelişim Dergisi, 36-45, (2006).
  • [10] Kazemi K., Noorizadeh N., "Quantitative comparison of SPM, FSL, and brainsuite for brain MR image segmentation", Journal of Biomedical Physics & Engineering, 4(1): 13, (2014).
  • [11] Guo C., Ferreira D., Fink K., Westman E., Granberg T., "Repeatability and reproducibility of FreeSurfer, FSL-SIENAX and SPM brain volumetric measurements and the effect of lesion filling in multiple sclerosis", European Radiology, 29(3): 1355-1364, (2019).
  • [12] Jenkinson M., Beckmann C.F., Behrens T.E., Woolrich M.W., Smith S.M., "Fsl", Neuroimage, 62(2):782-790, (2012).
  • [13] Fischl B., "FreeSurfer", Neuroimage, 62(2):774-781, (2012).
  • [14] Friston K.J., "Statistical Parametric Mapping", Neuroscience Databases: Springer, 237-250, (2003).
  • [15] Öziç M.Ü., Özşen S., "A new model to determine asymmetry coefficients on MR images using PSNR and SSIM", International Artificial Intelligence and Data Processing Symposium (IDAP), IEEE, Malatya, TURKEY, 1-6, (2017).
  • [16] Derflinger S., Sorg C., Gaser C., Myers N., Arsic M., Kurz A., Zimmer C., Wohlschläger A.,Mühlau M., "Grey-matter atrophy in Alzheimer's disease is asymmetric but not lateralized", Journal of Alzheimer's Disease, 25(2): 347-357, (2011).
  • [17] Öziç M.Ü., "3B Alzheimer MR Görüntülerinin Sınıflandırılmasında Yeni Yaklaşımlar", Doktora Tezi, Elektrik-Elektronik Mühendisliği, Selçuk Üniversitesi, Fen Bilimleri Enstitüsü, (2018).
  • [18] Mahmood R., Ghimire B., "Automatic detection and classification of Alzheimer's Disease from MRI scans using principal component analysis and artificial neural networks", 20th International Conference Systems, Signals and Image Processing (IWSSIP), 133-137: IEEE, (2013).
  • [19] Jha D., Kim J-I., Kwon G-R., "Diagnosis of Alzheimer’s Disease Using Dual-Tree Complex Wavelet Transform, PCA, and Feed-Forward Neural Network", Journal of Healthcare Engineering, 2017, (2017).
  • [20] Maldjian J.A., Laurienti P.J., Kraft R.A., Burdette J.H., "An automated method for neuroanatomic and cytoarchitectonic atlas-based interrogation of fMRI data sets", Neuroimage, 19(3): 1233-1239, (2003).
  • [21] Maldjian J.A., Laurienti P.J., Burdette J.H., "Precentral gyrus discrepancy in electronic versions of the Talairach atlas", Neuroimage, 21(1): 450-455, (2004).
  • [22] Rorden C., "MRIcro"., https://people.cas.sc.edu/ rorden/ mricro/mricro.html (14.02.2020)
  • [23] Kurth F., Luders E., Gaser C., "VBM8 toolbox manual", Jena: University of Jena, (2010).
  • [24] Marcus D.S., Wang T.H., Parker J., Csernansky J.G., Morris J.C., Buckner R.L., "Open Access Series of Imaging Studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults", Journal of Cognitive Neuroscience, 19(9): 1498-1507, (2007).
  • [25] Kurth F., Gaser C., Luders E., "A 12-step user guide for analyzing voxel-wise gray matter asymmetries in statistical parametric mapping (SPM)",Nature Protocols, 10(2): 293-304, (2015).
  • [26] Öziç M.Ü., Özşen S., "3D Electronic Brain Atlas Model For The Detection Of Neurological Disorders", Electronics World, 123(1973): 26-29, (2017).
  • [27] Öziç M.Ü., Ekmekci A.H., Özşen S., "Atlas-Based Segmentation Pipelines on 3D Brain MR Images: A Preliminary Study", BRAIN. Broad Research in Artificial Intelligence Neuroscience, 9(4):129-140, (2018).
  • [28] Tzourio-Mazoyer N., Landeau B., Papathanassiou D., Crivello F., Etard O., Delcroix N., Mazoyer B.,Joliot M., "Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain", Neuroimage, 15(1): 273-289, (2002).
  • [29] Maldjian J., WFU PickAtlas version 3.0 User Manual. Available:http://fmri.wfubmc.edu/downloads/WFUPick AtlasUserManualv3.0.pdf, (11.09.2017)
  • [30] Öziç M.Ü., Özşen S., "Üç Boyutlu T1 Ağırlıklı Manyetik Rezonans Görüntülerinde Ön İşleme Yöntemleri", Avrupa Bilim ve Teknoloji Dergisi, 19, 227-240, (2020).
  • [31] Mechelli A., Price C.J., Friston K.J., Ashburner J., "Voxel-based morphometry of the human brain: methods and applications", Current Medical Imaging Reviews, 1(2): 105-113, (2005).
  • [32] Ashburner J., "A fast diffeomorphic image registration algorithm", Neuroimage, 38(1):95- 113, (2007).
  • [33] Klein A., Andersson J., Ardekani B.A., Ashburner J., Avants B., Chiang M-C., Christensen G.E., Collins D.L., Gee J., Hellier P., "Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration", Neuroimage, 46(3):786-802, (2009).
  • [34] K-Lab., "How to calculate grey/white matter volume from segmented images", https://www.nemotos.net /?p=292 (10.02.2020).
  • [35] Ridgway G., "Miscellaneous useful MATLAB scripts for SPM/VBM",http://www0.cs.ucl.ac.uk/staff/g.ridgway/vbm/get_totals.m (10.02.2020).
  • [36] Killiany R.J., Moss M.B., Albert M.S., Sandor T., Tieman J., Jolesz F.,"Temporal lobe regions on magnetic resonance imaging identify patients with early Alzheimer's disease", Archives of Neurology, 50(9): 949-954, (1993).
  • [37] Polat F., Kumral E., "Normal ve patolojik beyin yaşlanması", Ege Tıp Dergisi, 49, (2010).
  • [38] Del Ser T., Hachinski V., Merskey H., Munoz D.G., "Alzheimer's disease with and without cerebral infarcts", Journal of the Neurological Sciences, 231(1-2): 3-11, (2005).
  • [39] Dai Z., Yan C., Wang Z., Wang J., Xia M., Li K., He Y., "Discriminative analysis of early Alzheimer's disease using multi-modal imaging and multi-level characterization with multi-classifier(M3)",Neuroimage, 59(3): 2187-2195, ( 2012).
  • [40] Julkunen V., Niskanen E., Muehlboeck S., Pihlajamäki M., Könönen M., Hallikainen M., Kivipelto M., Tervo S., Vanninen R., Evans A., "Cortical thickness analysis to detect progressive mild cognitive impairment: a reference to Alzheimer’s disease", Dementia and Geriatric Cognitive Disorders, 28(5):389-397, (2009).
  • [41] Thomann P.A., Dos Santos V., Toro P., Schönknecht P., Essig M., Schröder J., "Reduced olfactory bulb and tract volume in early Alzheimer's disease-a MRI study", Neurobiology of Aging, 30(5): 838-841, (2009).
  • [42] Hänggi J., Streffer J., Jäncke L., Hock C., "Volumes of lateral temporal and parietal structures distinguish between healthy aging, mild cognitive impairment, and Alzheimer's disease", Journal of Alzheimer's Disease, 26(4): 719-734, (2011).
  • [43] Voevodskaya O., Simmons A., Nordenskjöld R., Kullberg J., Ahlström H., Lind L., Wahlund L-O., Larsson E-M., Westman E., "The effects of intracranial volume adjustment approaches on multiple regional MRI volumes in healthy aging and Alzheimer's disease", Frontiers in Aging Neuroscience, 6: 264, (2014).
  • [44] Chu C., Hsu A-L., Chou K-H., Bandettini P., Lin C., "Does feature selection improve classification accuracy? Impact of sample size and feature selection on classification using anatomical magnetic resonance images", Neuroimage, 60(1):59-70, (2012).
  • [45] Budak H.,"Özellik Seçim Yöntemleri ve Yeni Bir Yaklaşım", Journal of Natural Applied Sciences, 22: 21-31, (2018).
  • [46] Saeys Y., Inza I., Larrañaga P., "A review of feature selection techniques in bioinformatics", Bioinformatics, 23(19): 2507-2517, (2007).
  • [47] Nguyen T., Nahavandi S., Creighton D., Khosravi A., "Mass spectrometry cancer data classification using wavelets and genetic algorithm", FEBS letters, 589(24): 3879-3886, (2015).
  • [48] Vakharia V., Gupta V., Kankar P., "A comparison of feature ranking techniques for fault diagnosis of ball bearing", Soft Computing, 20(4): 1601-1619, (2016). [49] Cortes C., Vapnik V.,"Support-vector networks", Machine Learning, 20(3):273-297, (1995).
  • [50] Magnin B., Mesrob L., Kinkingnéhun S., Pélégrini-Issac M., Colliot O., Sarazin M., Dubois B., Lehéricy S., Benali H., "Support vector machine-based classification of Alzheimer’s disease from whole-brain anatomical MRI", Neuroradiology, 51(2):73-83, (2009).
  • [51] Cuingnet R., Gerardin E., Tessieras J., Auzias G., Lehéricy S., Habert M-O., Chupin M., Benali H., Colliot O., "Automatic classification of patients with Alzheimer's disease from structural MRI: a comparison of ten methods using the ADNI database", Neuroimage, 56(2): 766-781, (2011).
  • [52] Ahmed O.B., Benois-Pineau J., Allard M., Amar C.B., Catheline G., "Classification of Alzheimer’s disease subjects from MRI using hippocampal visual features", Multimedia Tools and Applications,74(4):1249-1266, (2015).
  • [53] Liu J., Li M., Lan W., Wu F-X., Pan Y., Wang J., "Classification of Alzheimer's disease using whole brain hierarchical network", IEEE/ACM Transactions on Computational Biology and Bioinformatics, 15(2): 624-632, (2016).
  • [54] Tripathi S., Nozadi S.H., Shakeri M., Kadoury S., "Sub-cortical shape morphology and voxel based features for Alzheimer's disease classification", IEEE 14th International Symposium on Biomedical Imaging, 991-994: IEEE, (2017).
  • [55] Zeng N., Qiu H., Wang Z., Liu W., Zhang H., Li Y, "A new switching-delayed-PSO-based optimized SVM algorithm for diagnosis of Alzheimer’s disease", Neurocomputing, 320, 195-202, (2018).
  • [56] Savio A., Graña M., "An ensemble of classifiers guided by the AAL brain atlas for Alzheimer’s disease detection", International Work-Conference on Artificial Neural Networks, 107-114, (2013).
  • [57] Schmitter D., Roche A., Maréchal B., Ribes D., Abdulkadir A., Bach-Cuadra M., Daducci A., Granziera C., Klöppel S., Maeder P., "An evaluation of volume-based morphometry for prediction of mild cognitive impairment and Alzheimer's disease", NeuroImage: Clinical,7:7-17, (2015).
  • [58] de Vos F., Schouten T.M., Hafkemeijer A., Dopper E.G., van Swieten J.C., de Rooij M., van der Grond J., Rombouts S.A., "Combining multiple anatomical MRI measures improves Alzheimer's disease classification", Human Brain Mapping, 37(5): 1920-1929, (2016).
  • [59] Tae W.S., Kim S.S., Lee K.U., Nam E-C., Kim K.W., "Validation of hippocampal volumes measured using a manual method and two automated methods (FreeSurfer and IBASPM) in chronic major depressive disorder", Neuroradiology, 50(7): 569, (2008).
  • [60] Alemán-Gómez Y, "IBASPM: toolbox for automatic parcellation of brain structures", 12th Annual Meeting of the Organization for Human Brain Mapping, Florence, Italy, (2006).
  • [61] König T.,"Individual Brain Atlases using Statistical Parametric Mapping Software (IBASPM)", http://www. thomaskoenig.ch/Lester/ibaspm.htm (13.02.2020).
Toplam 60 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Araştırma Makalesi
Yazarlar

Muhammet Üsame Öziç 0000-0002-3037-2687

Hakan Ekmekci 0000-0002-5595-7251

Seral Özşen 0000-0001-5332-8665

Mücahid Barstuğan 0000-0001-9790-5890

Aydın Yıldoğan 0000-0002-9482-6203

Yayımlanma Tarihi 1 Mart 2022
Gönderilme Tarihi 4 Mayıs 2020
Yayımlandığı Sayı Yıl 2022 Cilt: 25 Sayı: 1

Kaynak Göster

APA Öziç, M. Ü., Ekmekci, H., Özşen, S., Barstuğan, M., vd. (2022). 3B T1 Ağırlıklı MR Görüntülerinde Atlas Tabanlı Hacim Ölçüm Yöntemini Kullanarak Alzheimer Hastalığının Teşhisi. Politeknik Dergisi, 25(1), 47-58. https://doi.org/10.2339/politeknik.728199
AMA Öziç MÜ, Ekmekci H, Özşen S, Barstuğan M, Yıldoğan A. 3B T1 Ağırlıklı MR Görüntülerinde Atlas Tabanlı Hacim Ölçüm Yöntemini Kullanarak Alzheimer Hastalığının Teşhisi. Politeknik Dergisi. Mart 2022;25(1):47-58. doi:10.2339/politeknik.728199
Chicago Öziç, Muhammet Üsame, Hakan Ekmekci, Seral Özşen, Mücahid Barstuğan, ve Aydın Yıldoğan. “3B T1 Ağırlıklı MR Görüntülerinde Atlas Tabanlı Hacim Ölçüm Yöntemini Kullanarak Alzheimer Hastalığının Teşhisi”. Politeknik Dergisi 25, sy. 1 (Mart 2022): 47-58. https://doi.org/10.2339/politeknik.728199.
EndNote Öziç MÜ, Ekmekci H, Özşen S, Barstuğan M, Yıldoğan A (01 Mart 2022) 3B T1 Ağırlıklı MR Görüntülerinde Atlas Tabanlı Hacim Ölçüm Yöntemini Kullanarak Alzheimer Hastalığının Teşhisi. Politeknik Dergisi 25 1 47–58.
IEEE M. Ü. Öziç, H. Ekmekci, S. Özşen, M. Barstuğan, ve A. Yıldoğan, “3B T1 Ağırlıklı MR Görüntülerinde Atlas Tabanlı Hacim Ölçüm Yöntemini Kullanarak Alzheimer Hastalığının Teşhisi”, Politeknik Dergisi, c. 25, sy. 1, ss. 47–58, 2022, doi: 10.2339/politeknik.728199.
ISNAD Öziç, Muhammet Üsame vd. “3B T1 Ağırlıklı MR Görüntülerinde Atlas Tabanlı Hacim Ölçüm Yöntemini Kullanarak Alzheimer Hastalığının Teşhisi”. Politeknik Dergisi 25/1 (Mart 2022), 47-58. https://doi.org/10.2339/politeknik.728199.
JAMA Öziç MÜ, Ekmekci H, Özşen S, Barstuğan M, Yıldoğan A. 3B T1 Ağırlıklı MR Görüntülerinde Atlas Tabanlı Hacim Ölçüm Yöntemini Kullanarak Alzheimer Hastalığının Teşhisi. Politeknik Dergisi. 2022;25:47–58.
MLA Öziç, Muhammet Üsame vd. “3B T1 Ağırlıklı MR Görüntülerinde Atlas Tabanlı Hacim Ölçüm Yöntemini Kullanarak Alzheimer Hastalığının Teşhisi”. Politeknik Dergisi, c. 25, sy. 1, 2022, ss. 47-58, doi:10.2339/politeknik.728199.
Vancouver Öziç MÜ, Ekmekci H, Özşen S, Barstuğan M, Yıldoğan A. 3B T1 Ağırlıklı MR Görüntülerinde Atlas Tabanlı Hacim Ölçüm Yöntemini Kullanarak Alzheimer Hastalığının Teşhisi. Politeknik Dergisi. 2022;25(1):47-58.
 
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