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Appl. Sci. 2017, 7(8), 831;

Improved Diagnostic Process of Multiple Sclerosis Using Automated Detection and Selection Process in Magnetic Resonance Imaging

Faculty of Engineering, Holon Institute of Technology (HIT), Holon 5810201, Israel
Faculty of Engineering, Bar-Ilan University, Ramat-Gan 52900, Israel
Author to whom correspondence should be addressed.
Received: 17 July 2017 / Revised: 6 August 2017 / Accepted: 11 August 2017 / Published: 13 August 2017
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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. View Full-Text
Keywords: MRI; Multiple Sclerosis; white matter; DICOM MRI; Multiple Sclerosis; white matter; DICOM

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Malka, D.; Vegerhof, A.; Cohen, E.; Rayhshtat, M.; Libenson, A.; Aviv Shalev, M.; Zalevsky, Z. Improved Diagnostic Process of Multiple Sclerosis Using Automated Detection and Selection Process in Magnetic Resonance Imaging. Appl. Sci. 2017, 7, 831.

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