Advanced 3D Methodology for Seizure Identification through Cubic Decimal Descriptor Pattern from MRI

Background: Volumetric three-dimensional (3D) magnetic resonance imaging (MRI) data are inherently complex, making feature extraction a significant challenge. The large volume of information in 3D MRI requires advanced algorithms to efficiently extract relevant features, which is crucial for identifying abnormal brain activity such as epileptic seizures. Methods: We propose a novel 3D methodology named the Cubic Decimal Descriptor Pattern (C-DDP) for enhanced feature extraction from 3D MRI data. To evaluate its effectiveness, we conducted experiments using a publicly available 3D MRI dataset comprising 85 individuals with focal cortical dysplasia type II (FCD II) and 85 healthy controls. Both 3D Fluid Attenuated Inversion Recovery (FLAIR) and T1-weighted isotropic image sequences were processed, and features were analyzed using various machine learning classifiers. Results: C-DDP consistently demonstrated superior performance in extracting discriminative features across different classifiers. The method improved the detection of FCD II and enabled more accurate identification of epileptogenic lesions compared to conventional feature extraction approaches. Conclusions: The proposed C-DDP approach represents a notable advancement in 3D MRI feature extraction, offering potential for enhanced diagnostic accuracy in patients with epileptogenic lesions. This methodology may contribute to more effective clinical decision-making and improved patient outcomes.