Multispectral Brain MRI Segmentation based on Fuzzy Classifiers and Evidence Theory
Publish place: 15th Iranian Conference on Electric Engineering
Publish Year: 1386
نوع سند: مقاله کنفرانسی
زبان: English
View: 1,681
This Paper With 5 Page And PDF Format Ready To Download
- Certificate
- من نویسنده این مقاله هستم
این Paper در بخشهای موضوعی زیر دسته بندی شده است:
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
ICEE15_005
تاریخ نمایه سازی: 17 بهمن 1385
Abstract:
Magnetic resonance imaging (MRI) techniques provide detailed anatomic information noninvasively and without the use of ionizing radiation. The
development of nau pulse seql.ences in MRI has allotyed obtaining images with high clinical importance and thtts joint analysis (multispectral MN) rr required for interpretation of these images. Fuzzy rule-based systems can combine many inpuls from widely varying sources so that they can be useful for description of tissues in the muhispectral MN. In a fuzry system, an error-free and optimized classifier can be obtained by genetic algorithms. In this paper, we have utilized a geneticfuzzy system for modeling dffirent tissues in brain MN as fuzzy classifers and have segmented the MR images by a combination ofthese classifiers using the evidence theory and the Dempster rule. Experiments were performed
using the simulated brain data (SBD) set. The numerical validation of the results demonstrates the strength of the proposed algorithm for medical image segmentation using either the evidence theory or a maximization process as the combination step.
Keywords:
Magnetic resonance imaging (MRI) , multispectral MRI , fuzzy system , genetic algorithm , Evidence Theory , Dempster rule
Authors
Hasanzadeh
Sharif University of Technology
Kasaei
Sharif University of Technology
مراجع و منابع این Paper:
لیست زیر مراجع و منابع استفاده شده در این Paper را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود Paper لینک شده اند :