Muhammet Üsame ÖZİÇ, Hakan EKMEKCİ, Seral ÖZŞEN, Mücahid BARSTUĞAN, Aydın YILDOĞAN

3B T1 Ağırlıklı MR Görüntülerinde Atlas Tabanlı Hacim Ölçüm Yöntemini Kullanarak Alzheimer Hastalığının Teşhisi

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.

Anahtar Kelimeler:

Alzhiemer, MR, Sınıflandırma, atlas tabanlı hacim ölçümü, özellik derecelendirme

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

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

Keywords:

Alzhiemer, MR, atlas-based volume measurement, feature ranking, classification,

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