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3B Alzheimer MR Görüntülerinin Hacimsel Kayıp Bölgelerindeki Voksel Değerleri Kullanılarak Sınıflandırılması

Year 2020, Volume: 7 Issue: 3, 1152 - 1166, 30.09.2020

Muhammet Üsame Öziç , Seral Özşen

https://doi.org/10.31202/ecjse.728049
Cited By: 5

Abstract

Alzheimer Hastalığı bilişsel bozukluklar ve unutkanlık ile başlayan ölümcül bir nörolojik hastalıktır. Hastalığın beyinde meydana getirdiği hacimsel değişimler yüksek çözünürlüklü manyetik rezonans görüntüleri ile izlenebilmektedir. Bu çalışmada, OASIS veri tabanından alınan 3B T1 ağırlıklı manyetik rezonans görüntüleri kullanılarak gri madde ve beyaz madde bölgelerinde meydana gelen hacimsel kayıplar voksel tabanlı morfometri yöntemi ile haritalandırılmış ve bu bölgelerdeki anlamlı voksel değerleri ile alzheimer ve normal manyetik rezonans görüntülerini sınıflandıran bir karar destek sistemi tasarlanmıştır. Manyetik rezonans görüntülerinde gruplar arası voksel tabanlı morfometri işlemi için SPM8, MRIcro programları ve VBM8 kütüphanesi kullanılmıştır. Hacimsel kayıp haritalarından elde edilen binary maskeler ile gri madde ve beyaz madde bölgeleri maskelenmiştir. Her bir gri madde ve beyaz madde görüntüsünde maske altında kalan bölgelerden aynı koordinat noktalarına denk gelen voksel değerleri ile anlamlı veri kümeleri oluşturulmuştur. Özellik derecelendirme yöntemleri ile veriler en anlamlı özellikten en anlamsız özelliğe doğru derecelendirilmiştir. Sıralanan özellikler on kat çapraz geçerleme ile lineer ve rbf kernel kullanan destek vektör makinelerine giriş olarak verilmiştir. Yapılan denemeler sonucunda en yüksek doğruluk oranları t-test özellik derecelendirme tabanlı lineer destek vektör makineleri ile gri madde sınıflandırmada %92.857 ve beyaz madde sınıflandırmada %79.286 olarak bulunmuştur.

Keywords

Manyetik Rezonans Alzheimer Voksel Tabanlı Morfometri Sınıflandırma

References

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  • Öziç M Ü., "3B Alzheimer MR Görüntülerinin Sınıflandırılmasında Yeni Yaklaşımlar", Doktora Tezi, Fen Bilimleri Enstitüsü, Selçuk Üniversitesi, 2018.
  • Pellegrini E., Ballerini L., Hernandez M d C V., Chappell F M., González-Castro V., Anblagan D., Danso S., Muñoz-Maniega S., Job D.,Pernet C, "Machine learning of neuroimaging for assisted diagnosis of cognitive impairment and dementia: A systematic review," Alzheimer's Dementia: Diagnosis, Assessment Disease Monitoring, 2018, vol. 10, pp. 519-535.
  • Lahmiri S.,Shmuel A., "Performance of machine learning methods applied to structural MRI and ADAS cognitive scores in diagnosing Alzheimer’s disease," Biomedical Signal Processing, 2019, vol. 52, pp. 414-419.
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  • 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, vol. 2017, 2017.
  • Wang S-H., Zhang Y., Li Y-J., Jia W-J., Liu F-Y., Yang M-M.,Zhang Y-D., "Single slice based detection for Alzheimer’s disease via wavelet entropy and multilayer perceptron trained by biogeography-based optimization", Multimedia Tools and Applications, 2016, pp. 1-25.
  • Mechelli A., Price C J., Friston K J.,Ashburner J., "Voxel-based morphometry of the human brain: methods and applications", Current medical imaging reviews, 2005, vol. 1, no. 2, pp. 105-113.
  • Öziç M Ü., Özşen S.,Ekmekci A H., "Voxel based morphometric analysis on MR images", in 2017 International Artificial Intelligence and Data Processing Symposium (IDAP), pp. 1-5: IEEE, 2017.
  • Öziç M Ü.,Özşen S., "Comparison Global Brain Volume Ratios on Alzheimer’s Disease Using 3D T1 Weighted MR Images," Avrupa Bilim ve Teknoloji Dergisi, 2020, no. 18, pp. 599-606.
  • Radua J., Canales-Rodríguez E J., Pomarol-Clotet E.,Salvador R., "Validity of modulation and optimal settings for advanced voxel-based morphometry", Neuroimage, 2014, vol. 86, pp. 81-90.
  • Dashjamts T., Yoshiura T., Hiwatashi A., Togao O., Yamashita K., Ohyagi Y., Monji A., Kamano H., Kawashima T., Kira J.,Honda H., "Alzheimer's Disease: Diagnosis by Different Methods of Voxel-Based Morphometry", Fukuoka Acta Medica, 2012, vol. 103, no. 3, pp. p59-69.
  • Beheshti I.,Demirel H., "Feature-ranking-based Alzheimer’s disease classification from structural MRI", Magnetic Resonance Imaging, 2016, vol. 34, no. 3, pp. 252-263.
  • Beheshti I.,Demirel H., "Probability distribution function-based classification of structural MRI for the detection of Alzheimer’s disease", Computers in Biology And Medicine, 2015, vol. 64, pp. 208-216.
  • Beheshti I., Demirel H., Farokhian F., Yang C.,Matsuda H., "Structural MRI-based detection of Alzheimer's disease using feature ranking and classification error", Computer Methods Programs in Biomedicine, 2016, vol. 137, pp. 177-193.
  • Beheshti I., Demirel H.,Matsuda H., "Classification of Alzheimer's disease and prediction of mild cognitive impairment-to-Alzheimer's conversion from structural magnetic resource imaging using feature ranking and a genetic algorithm", Computers in Biology And Medicine, 2017, vol. 83, pp. 109-119.
  • Savio A., García-Sebastián M T., Chyzyk D., Hernández C., Graña M., Sistiaga A., De Munain A L.,Villanúa J J C i b, "Neurocognitive disorder detection based on feature vectors extracted from VBM analysis of structural MRI", Computers in Biology Medicine, 2011, vol. 41, no. 8, pp. 600-610.
  • 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,2015, vol. 10, no. 2, pp. 293-304.
  • Ashburner J., "A fast diffeomorphic image registration algorithm", Neuroimage, 2007, vol. 38, no. 1, pp. 95-113.
  • 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, 2009, vol. 46, no. 3, pp. 786-802.
  • Baron J., Chetelat G., Desgranges B., Perchey G., Landeau B., De La Sayette V.,Eustache F, "In vivo mapping of gray matter loss with voxel-based morphometry in mild Alzheimer's disease", Neuroimage, 2001, vol. 14, no. 2, pp. 298-309.
  • Busatto G F., Garrido G E., Almeida O P., Castro C C., Camargo C H., Cid C G., Buchpiguel C A., Furuie S.,Bottino C M., "A voxel-based morphometry study of temporal lobe gray matter reductions in Alzheimer’s disease", Neurobiology of aging, 2003, vol. 24, no. 2, pp. 221-231.
  • Chu C., Hsu A-L., Chou K-H., Bandettini P., Lin C.,Initiative A s D N., "Does feature selection improve classification accuracy? Impact of sample size and feature selection on classification using anatomical magnetic resonance images", Neuroimage, 2012, vol. 60, no. 1, pp. 59-70.
  • Mwangi B., Tian T S.,Soares J C., "A review of feature reduction techniques in neuroimaging", Neuroinformatics, 2014, vol. 12, no. 2, pp. 229-244.
  • Nguyen T., Nahavandi S., Creighton D., Khosravi A., "Mass spectrometry cancer data classification using wavelets and genetic algorithm", FEBS letters, 2015, vol. 589, no. 24, pp. 3879-3886.
  • Cortes C.,Vapnik V., "Support-vector networks", Machine learning, 1995, vol. 20, no. 3, pp. 273-297.
  • Öziç M Ü., Özşen S., "T-test feature ranking based 3D MR classification with VBM mask", in 2017 25th signal processing and communications applications conference (SIU), pp. 1-4: IEEE, 2017.

Year 2020, Volume: 7 Issue: 3, 1152 - 1166, 30.09.2020

Muhammet Üsame Öziç , Seral Özşen

https://doi.org/10.31202/ecjse.728049
Cited By: 5

Abstract

References

  • Selekler K., "Alois Alzheimer ve Alzheimer Hastalığı," Türk Geriatri Dergisi, 2010, vol. 13, pp. 9-14.
  • Türkiye Alzheimer Derneği, "Türkiye'de 600bin aile Alzheimer Hastalığı ile Mücadele Ediyor.", http://www.alzheimerdernegi.org.tr/haber/turkiyede-600-bin-aile-alzheimer- hastaligi-ile-mucadele-ediyor/, 2020, (Erişim tarihi: 10.02.2020).
  • Association A s, "2019 Alzheimer's disease facts and figures," Alzheimer's & Dementia, 2019, vol. 15, no. 3, pp. 321-387.
  • TUİK, "Nüfus Projeksiyonları",http://www.tuik.gov.tr/PreHaberBultenleri.do?id=30567, 2018, (Ziyaret Tarihi: 06.04.2020).
  • Gürvit H., Baran B., "Demanslar ve Kognitif Bozukluklarda Ölçekler," Nöropsikiyatri Arşivi, 2007, vol. 44, pp. 58-65.
  • 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, 2007, vol. 19, no. 9, pp. 1498-1507.
  • MRIcro, https://people.cas.sc.edu/rorden/mricro/mricro.html, 2020, (Ziyaret tarihi: 10.02.2020).
  • UCL, SPM8, https://www.fil.ion.ucl.ac.uk/spm/software/spm8/, 2020, (Ziyaret Tarihi: 06.04.2020).
  • Kurth F., Luders E.,Gaser C., "VBM8 toolbox manual", 2010, Jena: University of Jena.
  • Öziç M Ü., "3B Alzheimer MR Görüntülerinin Sınıflandırılmasında Yeni Yaklaşımlar", Doktora Tezi, Fen Bilimleri Enstitüsü, Selçuk Üniversitesi, 2018.
  • Pellegrini E., Ballerini L., Hernandez M d C V., Chappell F M., González-Castro V., Anblagan D., Danso S., Muñoz-Maniega S., Job D.,Pernet C, "Machine learning of neuroimaging for assisted diagnosis of cognitive impairment and dementia: A systematic review," Alzheimer's Dementia: Diagnosis, Assessment Disease Monitoring, 2018, vol. 10, pp. 519-535.
  • Lahmiri S.,Shmuel A., "Performance of machine learning methods applied to structural MRI and ADAS cognitive scores in diagnosing Alzheimer’s disease," Biomedical Signal Processing, 2019, vol. 52, pp. 414-419.
  • Jo T., Nho K.,Saykin A J., "Deep learning in Alzheimer’s disease: diagnostic classification and prognostic prediction using neuroimaging data", Frontiers in aging neuroscience, 2019, vol. 11, p. 220.
  • Mahmood R.,Ghimire B., "Automatic detection and classification of Alzheimer's Disease from MRI scans using principal component analysis and artificial neural networks", in Systems, Signals and Image Processing (IWSSIP), 2013 20th International Conference on, pp. 133-137: IEEE, 2013.
  • 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, vol. 2017, 2017.
  • Wang S-H., Zhang Y., Li Y-J., Jia W-J., Liu F-Y., Yang M-M.,Zhang Y-D., "Single slice based detection for Alzheimer’s disease via wavelet entropy and multilayer perceptron trained by biogeography-based optimization", Multimedia Tools and Applications, 2016, pp. 1-25.
  • Mechelli A., Price C J., Friston K J.,Ashburner J., "Voxel-based morphometry of the human brain: methods and applications", Current medical imaging reviews, 2005, vol. 1, no. 2, pp. 105-113.
  • Öziç M Ü., Özşen S.,Ekmekci A H., "Voxel based morphometric analysis on MR images", in 2017 International Artificial Intelligence and Data Processing Symposium (IDAP), pp. 1-5: IEEE, 2017.
  • Öziç M Ü.,Özşen S., "Comparison Global Brain Volume Ratios on Alzheimer’s Disease Using 3D T1 Weighted MR Images," Avrupa Bilim ve Teknoloji Dergisi, 2020, no. 18, pp. 599-606.
  • Radua J., Canales-Rodríguez E J., Pomarol-Clotet E.,Salvador R., "Validity of modulation and optimal settings for advanced voxel-based morphometry", Neuroimage, 2014, vol. 86, pp. 81-90.
  • Dashjamts T., Yoshiura T., Hiwatashi A., Togao O., Yamashita K., Ohyagi Y., Monji A., Kamano H., Kawashima T., Kira J.,Honda H., "Alzheimer's Disease: Diagnosis by Different Methods of Voxel-Based Morphometry", Fukuoka Acta Medica, 2012, vol. 103, no. 3, pp. p59-69.
  • Beheshti I.,Demirel H., "Feature-ranking-based Alzheimer’s disease classification from structural MRI", Magnetic Resonance Imaging, 2016, vol. 34, no. 3, pp. 252-263.
  • Beheshti I.,Demirel H., "Probability distribution function-based classification of structural MRI for the detection of Alzheimer’s disease", Computers in Biology And Medicine, 2015, vol. 64, pp. 208-216.
  • Beheshti I., Demirel H., Farokhian F., Yang C.,Matsuda H., "Structural MRI-based detection of Alzheimer's disease using feature ranking and classification error", Computer Methods Programs in Biomedicine, 2016, vol. 137, pp. 177-193.
  • Beheshti I., Demirel H.,Matsuda H., "Classification of Alzheimer's disease and prediction of mild cognitive impairment-to-Alzheimer's conversion from structural magnetic resource imaging using feature ranking and a genetic algorithm", Computers in Biology And Medicine, 2017, vol. 83, pp. 109-119.
  • Savio A., García-Sebastián M T., Chyzyk D., Hernández C., Graña M., Sistiaga A., De Munain A L.,Villanúa J J C i b, "Neurocognitive disorder detection based on feature vectors extracted from VBM analysis of structural MRI", Computers in Biology Medicine, 2011, vol. 41, no. 8, pp. 600-610.
  • 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,2015, vol. 10, no. 2, pp. 293-304.
  • Ashburner J., "A fast diffeomorphic image registration algorithm", Neuroimage, 2007, vol. 38, no. 1, pp. 95-113.
  • 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, 2009, vol. 46, no. 3, pp. 786-802.
  • Baron J., Chetelat G., Desgranges B., Perchey G., Landeau B., De La Sayette V.,Eustache F, "In vivo mapping of gray matter loss with voxel-based morphometry in mild Alzheimer's disease", Neuroimage, 2001, vol. 14, no. 2, pp. 298-309.
  • Busatto G F., Garrido G E., Almeida O P., Castro C C., Camargo C H., Cid C G., Buchpiguel C A., Furuie S.,Bottino C M., "A voxel-based morphometry study of temporal lobe gray matter reductions in Alzheimer’s disease", Neurobiology of aging, 2003, vol. 24, no. 2, pp. 221-231.
  • Chu C., Hsu A-L., Chou K-H., Bandettini P., Lin C.,Initiative A s D N., "Does feature selection improve classification accuracy? Impact of sample size and feature selection on classification using anatomical magnetic resonance images", Neuroimage, 2012, vol. 60, no. 1, pp. 59-70.
  • Mwangi B., Tian T S.,Soares J C., "A review of feature reduction techniques in neuroimaging", Neuroinformatics, 2014, vol. 12, no. 2, pp. 229-244.
  • Nguyen T., Nahavandi S., Creighton D., Khosravi A., "Mass spectrometry cancer data classification using wavelets and genetic algorithm", FEBS letters, 2015, vol. 589, no. 24, pp. 3879-3886.
  • Cortes C.,Vapnik V., "Support-vector networks", Machine learning, 1995, vol. 20, no. 3, pp. 273-297.
  • Öziç M Ü., Özşen S., "T-test feature ranking based 3D MR classification with VBM mask", in 2017 25th signal processing and communications applications conference (SIU), pp. 1-4: IEEE, 2017.
There are 36 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Makaleler
Authors

Muhammet Üsame Öziç 0000-0002-3037-2687

Seral Özşen 0000-0001-5332-8665

Publication Date September 30, 2020
Submission Date April 28, 2020
Acceptance Date July 1, 2020
Published in Issue Year 2020 Volume: 7 Issue: 3

Cite

IEEE M. Ü. Öziç and S. Özşen, “3B Alzheimer MR Görüntülerinin Hacimsel Kayıp Bölgelerindeki Voksel Değerleri Kullanılarak Sınıflandırılması”, El-Cezeri Journal of Science and Engineering, vol. 7, no. 3, pp. 1152–1166, 2020, doi: 10.31202/ecjse.728049.

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