مشارکت سه بافت مغزی در تشخیص بیماری آلزایمر از MRI ساختاری
محورهای موضوعی : عمومىشیما تاج الدینی 1 , حبیب اله دانیالی 2 , محمدصادق هل فروش 3 , یعقوب فاطمی 4
1 - Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran
2 - Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran
3 - Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran
4 - Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran
کلید واژه: بیماری آلزایمر, نشانگر زیستی, طبقه بندی, استخراج ویژگی, تصویربرداری رزونانس مغناطیسی,
چکیده مقاله :
بیماری آلزایمر (AD) یک بیماری پیشرونده و برگشت ناپذیر است که به تدریج باعث می شود بیماران نتوانند کارهای روزمره خود را انجام دهند. اگرچه روش های درمانی فعلی نمی توانند بیماری را به طور کامل درمان کنند ، اما تشخیص به موقع آن می تواند علائم را کاهش داده و کیفیت زندگی بیماران را افزایش دهد. در ادبیات فعلی ، استفاده از بافت ماده خاکستری (GM) که به عنوان نشانگر زیستی مناسب شناخته می شود ، در تشخیص AD بسیار رایج است. با این حال ، به نظر می رسد دو بافت مغز دیگر معروف به مایع مغزی نخاعی (CSF) و ماده سفید (WM) اطلاعات مفیدی را درباره تغییرات مغزی بیماران نشان می دهند. هدف از مطالعه حاضر ایجاد یک سیستم اتوماتیک برای تشخیص زود هنگام بیماری آلزایمر از MRI ساختاری با در نظر گرفتن همزمان ویژگی های مناسب از تمام بافت های GM ، CSF و WM است. یک طبقه بندی SVM-RBF بر روی پایگاه داده OASIS آموزش داده شده و مورد ارزیابی قرار می گیرد تا AD از افراد سالم کنترل شود. نتایج به دست آمده نشان دهنده دقت و حساسیت بالاتر الگوریتم پیشنهادی در مقایسه با روش مشابه است
Alzheimer’s disease (AD) is a progressive and irreversible disease which gradually makes patients unable to do their daily routines. Although the present treatments can not cure the disease completely, its early detection can reduce symptoms and enhance the patients’ life quality. In the current literature, using the grey matter (GM) tissue which is known as an appropriate biomarker is highly common in AD diagnosis. However, two other brain tissues known as cerebrospinal fluid (CSF) and white matter (WM) seem to reveal beneficial information about the patients’ brain changes. The aim of the present study is to develop an automatic system for the early diagnosis of Alzheimer’s disease from structural MRI by simultaneously considering suitable features of all GM, CSF and WM tissues. A SVM-RBF classifier is trained and evaluated on the OASIS database to separate AD from healthy control (HC) subjects. The obtained results represent higher accuracy and sensitivity of the proposed algorithm in comparison with similar method.
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