Comparison Evaluation of Three Optimization Algorithms in MRF model for Brain Tumour Segmentation in MRIs
Publish place: 19th Iranian Conference on Electric Engineering
Publish Year: 1390
نوع سند: مقاله کنفرانسی
زبان: English
View: 986
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:
ICEE19_471
تاریخ نمایه سازی: 14 مرداد 1391
Abstract:
MRI brain segmentation plays an increasingly important role in diagnosis and treatment of diseases. Since MRI segmentation manually consumes valuable human resources, a great deal of efforts has been made to automate this process. MRF has been one of the most active research areas of MRI brain segmentation which seeks an optimal label field in a large space. The classical optimization algorithm is Simulated Annealing (SA) that could get the global optimal solution with heavy computation burden. Hence many efforts have been made to obtain the optimal solution in a reasonable time. In this paper, a comparison evaluation of two proposed optimal researching algorithms with the classical RF for brain tumour segmentation is presented. The first applies a combination of improve genetic algorithm (IGA) and SA, the second uses a hybrid of ant colony optimization (ACO) and gossiping algorithm. The obtained results can assist users to select the appropriate approach for tumour segmentation
Keywords:
Markov Random Field (MRF) , Simulated Annealing (SA) , Improved Genetic Algorithm (IGA) , Ant Colony Optimization (ACO) , Gossiping Algorithm , Tumour image segmentation
Authors
Sahar Yousefi
Shahrood University of Technology
Reza Azmi
Alzahra University
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 :