Improved Diagnostic Process of Multiple Sclerosis Using Automated Detection and Selection Process in Magnetic Resonance Imaging
Source abstract
Abstract
In this paper, we present a new method of displaying Magnetic Resonance (MR) images taken from Multiple Sclerosis (MS) patients. We show that our method can potentially make the diagnostic process far more focused and concise. The method is implemented as an algorithm-based application, which automatically detects MS lesions and reduces the amount of reviewed images by 98% or more. In contrast to existing detection algorithms, our application utilizes five different types of MR images as well as the Digital Imaging and Communications in Medicine (DICOM) standard, supporting a wide range of data sets. After images are selected for file type and relevant brain region, each image is subjected to four separate algorithms, the results of which are combined into a single displayed image for the use of the diagnosing physician.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Multiple Sclerosis Research Studies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Engineering, Holon Institute of Technology (HIT), Holon 5810201, Israel
Faculty of Engineering, Bar-Ilan University, Ramat-Gan 52900, Israel
References (22)
Show source-provided references
1. Medical image processing10.1016/j.ins.2005.01.005 ↗
2. Image processing and machine learning for fully automated probabilistic evaluation of medical images10.1016/j.cmpb.2010.06.021 ↗
3. The Clinical Epidemiology of Multiple Sclerosis10.1016/j.nic.2008.09.002 ↗
4. The Challenge of Multiple Sclerosis: How Do We Cure A Chronic Heterogeneous Disease?10.1002/ana.21640 ↗
5. Multiple sclerosis10.1016/S0140-6736(08)61620-7 ↗
6. Aminoff, M.J., Boller, F., and Swaab, D.F. (2014). Multiple Sclerosis and Related Disorders: Handbook of Clnical Neurology, Elsevier. [3rd ed.].
7. Multiple Sclerosis: The Role of MR Imaging
8. MRI studies of multiple sclerosis: Implications for the natural history of the disease and for monitoring efectiveness of experimental therapies10.1177/135245859600200406 ↗
9. Automated approaches for analysis of multimodal MRI acquisitions in a study of cognitive aging10.1016/j.cmpb.2011.03.010 ↗
10. BTK: An open-source toolkit for fetal brain MR image processing10.1016/j.cmpb.2012.08.007 ↗
11. Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria10.1002/ana.22366 ↗
12. Comparison of the 2010 and 2005 versions of the McDonald MRI criteria for dissemination-in-time in Taiwanese patients with classic multiple sclerosis10.1016/j.jns.2013.03.018 ↗
13. Magnetic resonance monitoring of lesion evolution in multiple sclerosis10.1177/1756285613484079 ↗
14. Comparison of MR Pulse Sequences in the Detection of Multiple Sclerosis Lesions
15. Detection of multiple sclerosis lesions using EPI-FLAIR images10.1016/S0730-725X(00)00166-1 ↗
16. Fully automatic segmentation of multiple sclerosis lesions in brain MR T2FLAIR images using adaptive mixtures method and markov random field model10.1016/j.compbiomed.2007.12.005 ↗
17. An automated tool for detection of FLAIR-hyperintense white-matter lesions in Multiple Sclerosis10.1016/j.neuroimage.2011.11.032 ↗
18. Automatic segmentation and volumetry of multiple sclerosis brain lesions from MR images10.1016/j.nicl.2015.05.003 ↗
19. Automatic detection and segmentation of evolving processes in 3D medical images: Application to multiple sclerosis10.1016/S1361-8415(02)00056-7 ↗
20. Review of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging10.1016/j.media.2012.09.004 ↗
21. Computer-Aided Diagnosis of Multiple Sclerosis10.1080/17486700802070724 ↗
22. Computer-Aided Diagnosis Systems for Brain Diseases in Magnetic Resonance Images10.3390/a2030925 ↗