An efficient multiple sclerosis segmentation and detection system using neural networks
An efficient multiple sclerosis segmentation and detection system using neural networks
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DOI：
10.1016/j.compeleceng.2018.07.020
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发表时间：
2018-10-01
影响因子：
4.3
通讯作者：
Abed, Sa'ed
中科院分区：
文献类型：
--
作者：
Alshayeji, Mohammad H.;Al-Rousan, Mohammad A.;Abed, Sa'ed
在这项工作中，提出了一种有效的多发性硬化症（MS）分割技术，以简化预处理步骤并缩短非均匀单通道磁共振成像（MRI）的处理时间。使用全局阈值算法有效地应用了空间滤波图像映射、直方图参考图像和直方图匹配技术来获得每个图像的局部阈值。使用数学和形态学运算进行特征提取，并使用多层前馈神经网络（MLFFNN）识别多发性硬化症组织。流体衰减反演恢复（FLAIR）系列用于集成更快的系统，同时保持可靠性和准确性。在质谱检测系统中首次提出了一种基于矢状面（SAG） FLAIR的系统，该系统减少了图像的使用数量，并将处理时间缩短了近三分之一。我们的检测系统提供了显著的识别率高达98.5%。此外，在测试新图像时，观察到相对较高的骰子系数（DC）值（0.71 +/- 0.18）。
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 (0.71 +/- 0.18) was observed upon testing new images.