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For lesion detection in multiple sclerosis (MS), the magnetic resonance imaging (MRI) technique is a well-established tool with great sensitivity. Two methods to study this disease are applied in this paper: the Decimal Descriptor Pattern (DDP) along with the Local Binary Pattern (LBP). These methods are applied in order to identify the main features of the tissue. A set of layers is exploited to characterize 3D MRI of the brain. We have extended the DDP operator to increase its MRI performance and demonstrated that this extended operator is efficient in terms of computation. The robust feature selected methods are compared by means of the Support Vector Machine (SVM). The findings show that our solution is both robust and fast.

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Identification of Lesions in Multiple Sclerosis Based on the Decimal Descriptor Pattern

  • 会议论文

  • 发表日期：

    2025年

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  • Samah Yahia,
  • Yassine Ben Salem,
  • Mohamed Naceur Abdelkrim

摘要

For lesion detection in multiple sclerosis (MS), the magnetic resonance imaging (MRI) technique is a well-established tool with great sensitivity. Two methods to study this disease are applied in this paper: the Decimal Descriptor Pattern (DDP) along with the Local Binary Pattern (LBP). These methods are applied in order to identify the main features of the tissue. A set of layers is exploited to characterize 3D MRI of the brain. We have extended the DDP operator to increase its MRI performance and demonstrated that this extended operator is efficient in terms of computation. The robust feature selected methods are compared by means of the Support Vector Machine (SVM). The findings show that our solution is both robust and fast.