مجله علمی  رایانش نرم و فناوری اطلاعات

مجله علمی رایانش نرم و فناوری اطلاعات

بخش‌بندی بافت‌های مغزی از روی تصاویر MRI با استفاده از مدل بهبودیافته‌ی الگوریتم خوشه‌بند گستافسون-کسل

نوع مقاله : مقاله پژوهشی فارسی

نویسندگان
1 گروه علوم مهندسی، دانشکده فناوری‌های نوین، دانشگاه محقق اردبیلی، نمین، ایران.
2 گروه بیوالکتریک، دانشکده مهندسی پزشکی، دانشگاه صنعتی سهند، تبریز، ایران.
چکیده
بخش‌بندی تصاویر MRI تحت تغییرات غیریکنواختی شدت و نویز یک مسئله‌ی چالش‌برانگیز است. الگوریتم فازی C-میانگین یکی از محبوب‌ترین الگوریتم‌های خوشه‌بندی بوده که نسبت به نویز حساس بوده و آهنگ همگرایی آن تحت تاثیر توزیع داده قرار دارد. این الگوریتم با به‌کارگیری فاصله‌ی اقلیدسی، تغییرات فاصله‌ی نقاط داده در خوشه‌های فشرده و مشابه را نادیده می‌گیرد. برای حل این چالش‌ها، تابع هدف جدیدی براساس اندازه‌گیری معیار تشابه از طریق فاصله‌ی ماهالانوبیس در خوشه‌بندی گستافسون-کسل ارائه می‌شود که نسبت به رویکردهای مبتنی بر فازی C-میانگین مقاوم به نویز و غیریکنواختی شدت است. همچنین، به‌منظور افزایش استحکام الگوریتم پیشنهادی به نویز و افزایش دقت بخش‌بندی بافت‌های مغزی، تئوری اطلاعات به‌کار گرفته می‌شود. الگوریتم گستافسون-کسل استاندارد معمولاً تحت تاثیر مقدار فازی‌شدگی قرار می‌گیرد. برای حل این مسئله، عبارت آنتروپی تابع عضویت به‌کار گرفته می‌شود. در الگوریتم پیشنهادی، جهت حل چالش‌های مربوط به نویز و حفظ بهتر جزئیات تصویر، رویکرد بازسازی مورفولوژیکی به‌عنوان یک مرحله‌ی پیش‌پردازش به‌کار گرفته می‌شود. نتایج نشان داد که روش پیشنهادی در مقایسه با روش‌های بخش‌بندی فازی قبلی یک الگوریتم دقیق برای بخش‌بندی بافت‌های چندگانه‌ی مغزی در سطوح مختلف نویز و غیریکنواختی شدت است.
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