کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6883246 | 1444169 | 2018 | 15 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
An efficient multiple sclerosis segmentation and detection system using neural networks
ترجمه فارسی عنوان
یک سیستم تقسیم بندی و سیستم تشخیص مولتیپل اسکلروزیس کارآمد با استفاده از شبکه های عصبی
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
شبکه های کامپیوتری و ارتباطات
چکیده انگلیسی
In this work, an efficient multiple sclerosis (MS) segmentation technique is proposed to simplify pre-processing steps and diminish processing time using heterogeneous single-channel magnetic resonance imaging (MRI). A spatial-filtering image mapping, histogram reference image, and histogram matching techniques are effectively applied to possess a local threshold per image using the global threshold algorithm. Feature extraction is performed using mathematical and morphological operations, and a multilayer feed-forward neural network (MLFFNN) is used identify multiple sclerosis' tissues. Fluid-attenuated inversion recovery (FLAIR) series are used to integrate a faster system while maintaining reliability and accuracy. A sagittal (SAG) FLAIR-based system is proposed for the first time in MS detection systems, which reduces the number of utilized images, and decreases the processing time by nearly one-third. Our detection system provided a significant recognition rate of up to 98.5%. Moreover, a relatively high dice coefficient (DC) value (0.71â¯Â±â¯0.18) was observed upon testing new images.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computers & Electrical Engineering - Volume 71, October 2018, Pages 191-205
Journal: Computers & Electrical Engineering - Volume 71, October 2018, Pages 191-205
نویسندگان
Mohammad H. Alshayeji, Mohammad A. Al-Rousan, Hanem Ellethy, Sa'ed Abed,