تشخیص تومورهای مغزی از تصاویر تشدید مغناطیسی با تلفیق روش‌های ‌سوپر‌پیکسل و طبقه‌بندی ماشین بردار رابط

نوع مقاله: مقاله پژوهشی

نویسندگان

1 کارشناس ارشد - مهندسی برق الکترونیک، واحد مبارکه، دانشگاه آزاد اسلامی، مبارکه، ایران

2 استادیار-گروه برق، دانشکده فنی مهندسی، دانشگاه آزاد اسلامی واحد مبارکه، مبارکه ، اصفهان، ایران

چکیده

تولید سلول‌های اضافی اغلب تشکیل توده‌ای از بافت را می‌دهند که به آن تومور اطلاق می‌شود. تومورها می‌توانند عملکرد صحیح مغز را مختل کنند و حتی منجر به مرگ بیمار گردند. یکی از راه‌های تشخیصی غیرتهاجمی برای این بیماری تصویر‌برداری تشدید مغناطیسی (MRI) می‌باشد. توسعه‌ی یک سیستم تشخیصی اتوماتیک یا نیمه‌اتوماتیک به کمک کامپیوتر در درمان‌های پزشکی مورد نیاز است. الگوریتم‌های متعددی برای تشخیص تومور بکار گرفته شده است که هرکدام دارای مزایا و معایب خاص خودش است. در این پژوهش، از تلفیق روش‌های تقسیم‌بندی سوپرپیکسل و طبقه‌بندی RVM، یک روش اتوماتیک برای پیدا کردن محدوده دقیق ناحیه تومور در تصویر MRI ابداع نموده است. الگوریتم مورد‌استفاده در روش سوپرپیکسل، الگوریتم SLIC است که برای هر سوپرپیکسل 13 ویژگی آماری و شدت روشنائی، محاسبه شده و در نهایت توسط الگوریتم طبقه‌بندی RVM روشی آموزش داده می‌شود که بتواند در هر تصویر MRI مغز، قسمت تومور را از غیر‌تومور تشخیص دهد.در این تحقیق از مجموعه داده BRATS2012 و از تصاویر با وزن FLAIR استفاده شده است و نتایج بدست آمده با نتایج BRATS2012 مقایسه گردیده است و ضرایب هم‌پوشانی Dice، BF score و Jaccard به ترتیب 0.898 ، 0.697 و 0.754 بدست آمده است.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Detection Of Brain Tumors From Magnetic Resonance Imaging By Combining Superpixel Methods And Relevance Vector Machines Classification (RVM)

نویسندگان [English]

  • Ebrahim Akbari 1
  • Mehran Emadi 2
1 Electrical Engineering, Faculty of Engineering, Islamic Azad University, Mobarakeh Unit, Iran
2 Assistant Professor Islamic Azad University Mobarakeh Branch , Mobarakeh, Isfahan, Iran
چکیده [English]

The production of additional cells often forms a mass of tissue that is referred to as a tumor. Tumors can disrupt the proper functioning of the brain and even lead to the patients' death. One of the non-invasive diagnostic methods for this disease is Magnetic Resonance Imaging (MRI). The development of an automated or semi-automatic diagnostic system is required by the computer in medical treatments. Several algorithms have been used to detect a tumor, each with its own advantages and disadvantages. In the present study, an automatic method has been developed by the combination of new methods in order to find the exact area of the tumor in the MRI image. This algorithm is based on super pixel and RVM classification. The algorithm used in the super pixel method is the SLIC algorithm, which calculates for each super pixel 13 statistical characteristics and severity. Finally, an educational method introduced from the RVM classification algorithm that can detect the tumor portion from non-tumor in each brain MRI image. BRATS2012 dataset and FLAIR weights have been utilized in this study The results are compared with the results of the BRATS2012 data and The overlap coefficients of Dice, BF score, and Jaccard were 0.898, 0.697 and 0.754, respectively.

کلیدواژه‌ها [English]

  • Magnetic resonance imaging
  • Super pixel classification
  • Relevance vector machines classification
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