نوع مقاله: مقاله کامل پژوهشی

نویسندگان

1 کارشناس ارشد، گروه مهندسی پزشکی، دانشکده مهندسی برق، دانشگاه صنعتی شریف

2 استادیار، گروه مهندسی پزشکی، دانشکده مهندسی برق، دانشگاه صنعتی شریف

10.22041/ijbme.2009.13383

چکیده

در این مقاله روشی برای تشخیص بیماری آلزایمر پیشنهاد شده است که با استفاده از تحلیل تصاویر MR مغزی، شامل دو گروه وزن دار با T1 و وزن دار با T2، افراد بیمار را شناسایی می کند. با توجه به تفاوت های ماهیتی میان تصاویر وزن دار باT1 و وزن دار با T2، ویژگی های متفاوتی را از آنها استخراج نموده، سپس با بررسی مقادیر ویژه حاصل از این ویژگی ها، ابعاد فضای آنالیز را کاهش دادیم و خروجی را به دو تفکیک کننده غیرخطی اعمال کردیم. بدین ترتیب به طور همزمان و موازی، تصاویر وزن دار با T1 و وزن دار با T2 تحلیل شدند. سپس چون بیماری آلزایمر بخش خاکستری و سفید مغز را بیش از قسمت های سیاه و حاشیه ای آن (مانند بخش هایی موسوم به برآمدگی های چین خورده مغز و همچنین سینوس ها) مورد هجوم قرار می دهد و همچنین با توجه به اینکه تصاویر وزن دار با T1 حاوی اطلاعات بخش خاکستری و سفید مغز و تصاویر وزن دار با T2 حاوی اطلاعات مربوط به بخش خاکستری و سیاه است، نتایج به دست آمده از تصاویر وزن دار با T1را به تصاویر وزن دار با T2 ارجحیت داده، وزن بیشتری را به آن اختصاص دادیم تا پاسخ نهایی حاصل شود. در این مقاله از 120 تصویر وزن دار با T1 و وزن دار با T2 شامل مقطع های مختلف مغزی استفاده شده است که بعد از انطباق، به دو بخش آموزشی و آزمون طبقه بندی شده اند. نتایج، حاکی از تشخیص صحیح حدود سه چهارم تصاویر است.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Alzheimer's Disease Diagnosis Using Nonlinear Weighted T1-Mri Classification

نویسندگان [English]

  • Meysam Torabi 1
  • Emadoddin Fatemizadeh 2

1 M.Sc Graduated, Biomedical Engineering Group, Electrical Engineering School, Sharif University of Technology

2 Assistant Professor, Biomedical Engineering Group, Electrical Engineering School, Sharif University of Technology

چکیده [English]

In this paper, an MRI-based diagnosing approach has been proposed which simultaneously analyzes T1-MR and T2-MR images. The dataset contains 120 cross-sectional images of abnormal and also normal brains as control group. Due to inherent proprieties of T1 and T2 images and their principal differences, particular features have been extracted from each image. Then, more meaningful data has been structured by automatically eliminating redundant data and generating a semi-linear combination of the remaining features. Considering the fact that Alzheimer's disease mainly damages the gray and white matter of the brain and knowing that these parts of the brain can be more clearly observed in T1 images, the classifier which works under a nonlinear structure, allocates more weight for processing the T1 images comparing to T2 image. The images, after being registered, have been processed in two groups of training and test sets. According to the results, three forth of the dataset which was obtained from Harvard University's dataset (The Whole Brain Atlas) has been correctly diagnosed.

کلیدواژه‌ها [English]

  • Alzheimer's disease
  • Brain Images
  • Feature Extraction
  • Nonlinear Classifier

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