بازیابی تصاویر پزشکی بر اساس محتوا با استفاده از نگاشت ویژگی‌های تصاویر در سطح بازخورد ربط
محورهای موضوعی : پردازش تصویر و ویدئو
1 - دانشگاه آزاد اسلامی، واحد نجف‌آباد
2 - دانشگاه آزاد اسلامی واحد نجف آباد
کلید واژه: ماشین بردار پشتیبان, آنالیز مؤلفه‌های اصلی, بازخورد ربط, شکاف معنایی, بازیابی تصاویر بر اساس محتوا, آنالیز تفکیک کننده خطی,
چکیده مقاله :
هدف از این تحقیق طراحی یک سیستم بازیابی تصاویر پزشکی مبتنی بر محتوا و ارائه روشی نوین برای کاهش شکاف معنایی موجود بین ویژگی‌های دیداری و مفاهیم مورد جستجوی کاربر می‌باشد. به طور کلی عملکرد سیستم‌های بازیابی تصویر تنها بر اساس ویژگی‌های دیداری کاهش می‌یابد چرا که این ویژگی‌ها اغلب در توصیف مفاهیم معنایی تصویر ناتوان‌اند. در این تحقیق این مشکل با ارائه راهکاری نوین در سطح بازخورد ربط و با استفاده از انتقال فضای ویژگی‌های تصاویر مرتبط و غیر مرتبط به فضایی جدیدتر، با ابعاد کمتر و دارای همپوشانی کمتر مرتفع می‌گردد. برای این منظور با استفاده از تکنیک‌های آنالیز مؤلفه‌های اصلی(PCA) و آنالیز تفکیک‌کننده خطی (LDA) فضای ویژگی‌ها را تغییر داده و سپس با بهره‌گیری از ماشین بردار پشتیبان (SVM) به طبقه‌بندی تصاویر مرتبط و غیرمرتبط می‌پردازیم. الگوریتم ارائه شده بر روی پایگاه داده‌ای شامل 10000 تصویر اشعه X پزشکی از 57 کلاس معنایی ارزیابی شده است. نتایج به دست آمده نشان می‌دهد که الگوریتم ارائه شده به طور قابل توجهی دقت سیستم بازیابی را بهبود می‌بخشد.
The purpose of this study is to design a content-based medical image retrieval system and provide a new method to reduce semantic gap between visual features and semantic concepts. Generally performance of the retrieval systems based on only visual contents decrease because these features often fail to describe the high level semantic concepts in user’s mind. In this paper this problem is solved using a new approach based on projection of relevant and irrelevant images in to a new space with low dimensionality and less overlapping in relevance feedback level. For this purpose, first we change the feature space using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) techniques and then classify the feedback images applying Support Vector Machine (SVM) classifier. The proposed framework has been evaluated on a database consisting of 10,000 medical X-ray images of 57 semantic classes. The obtained results show that the proposed approach significantly improves the accuracy of retrieval system.
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