An efficient multiple sclerosis segmentation and detection system using neural networks. (October 2018)

• Record Type: Journal Article
• Authors: Alshayeji, Mohammad H.; Al-Rousan, Mohammad A.; Ellethy, Hanem; Abed, Sa'ed
• Is Part Of: Computers & electrical engineering. Volume 71(2018)
• Page Start: 191
• Page End: 205
• Publication Date: 2018-10
• Subjects: Fluid-attenuated inversion recovery (FLAIR) -- Global threshold -- Histogram matching -- Multilayer feed-forward neural network (MLFFNN) -- Multiple sclerosis (MS) -- Magnetic resonance imaging (MRI); Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2018.07.020
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: 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.