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Copyright © 2022 Abdulsattar Abdullah Hamad et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0/

Abstract

Magnetic Resonance Imaging to detect its lesions is used to diagnose multiple sclerosis. Experts usually perform this detection process manually, but there is interest in automating it to speed up the diagnosis and monitoring of this disease. A variety of automatic image segmentation methods have been proposed to quickly detect these lesions. A Gaussian Mixture Model is first constructed to identify outliers in each image. Then, using a set of rules based on expert knowledge of multiple sclerosis lesions, those outliers of the model that do not match the lesions' characteristics are discarded. Furthermore, segmented lesions usually correspond to gray matter-rich brain regions. In some cases, false positives can be detected, but the rules used cannot eliminate all errors without jeopardizing the segmentation’s quality. The second method involves training a convolutional neural network (CNN) that can segment lesions based on a set of training images. This technique can learn a set of filters that, when applied to small sections of an image called “patches,” produce a set of characteristics that can be used to classify each voxel of the image as a lesion or healthy tissue. On the other hand, the results show that the networks are capable of producing results in the worked database comparable to those produced by the algorithms in the literature.

Details

Title
Using Convolutional Neural Networks for Segmentation of Multiple Sclerosis Lesions in 3D Magnetic Resonance Imaging
Author
Abdulsattar Abdullah Hamad 1   VIAFID ORCID Logo  ; Mustafa Musa Jaber 2 ; Mohammed Altaf Ahmed 3   VIAFID ORCID Logo  ; Abdulsahib, Ghaida Muttashar 4 ; Osamah Ibrahim Khalaf 5 ; Meraf, Zelalem 6   VIAFID ORCID Logo 

 Department of Medical Laboratory Techniques, Dijlah University College, Baghdad 10021, Iraq; Department of Medical Laboratory Techniques, Al-Turath University College, Baghdad 10021, Iraq 
 Department of Medical Instruments Engineering Techniques, Dijlah University College, Baghdad 10021, Iraq; Department of Medical Instruments Engineering Techniques, Al-Farahidi University, Baghdad 10021, Iraq 
 Department of Computer Engineering, College of Computer Engineering & Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia 
 Department of Computer Engineering, University of Technology, Baghdad, Iraq 
 Al-Nahrain Nanorenewable Energy Research Center, Al-Nahrain University, Baghdad, Iraq 
 Department of Statistics, Injibara University, Injibara, Ethiopia 
Editor
Palanivel Velmurugan
Publication year
2022
Publication date
2022
Publisher
John Wiley & Sons, Inc.
ISSN
16878434
e-ISSN
16878442
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2658000346
Copyright
Copyright © 2022 Abdulsattar Abdullah Hamad et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0/