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Department of Computer Engineering, Abhar Branch, Islamic Azad University, Abhar, Iran

https://orcid.org/0000-0002-2990-9598

Department of Computer Engineering, Karaj Branch, Islamic Azad University, Karaj, Iran

E-mail Address: m_mirhosseini@sbu.ac.ir

Corresponding author.

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Reyhaneh Aliakbari

Department of Rehabilitation Sciences, College of Rehabilitation Sciences, University of Manitoba, Winnipeg, MB, Canada

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https://doi.org/10.1142/S1469026824500081Cited by:0 (Source: Crossref)

Abstract

Multiple Sclerosis (MS) is one of the debilitating disorders of the central nervous system. This disease causes lesions in the white matter of the brain tissue. It can also lead to many physical and psychological disorders in movement, vision, and memory. Lesion segmentation in MRI images to determine the number and size of lesions is one of the diagnostic problems for specialists. Using automated diagnostic tools as an aid can help professionals. Traditional image processing and deep learning methods are used to automate lesion segmentation. The U-Net is one of the most widely used deep learning architectures for MS lesion segmentation. The images are used in the Fourier domain in the U-Net network, which does not include all its features. Our proposed method combines the HAR wavelet transform and the Dense net-based U-Net. This makes local features and lesions of different sizes more prominent and leads to higher quality segmentation. The proposed method had a better Dice value than the compared methods in the experiments.

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