A Novel Preprocessing Model for Multi Modal Brain MRI image Classification for Stroke Prognosis

Title

A Novel Preprocessing Model for Multi Modal Brain MRI image Classification for Stroke Prognosis

Creator

Joseph, Alwin; Chandra, J.

Description

Magnetic Resonance Imaging (MRI) is an imaging technique used for the diagnosis and observing the progression in various neurological disorders. Stroke is one of the prominent neurological disorders that creates significant impacts in the patients. It occurs when the blood supply to part of the brain is interrupted or reduced, preventing brain tissues from getting oxygen and nutrients. Multimodal data from various modalities help clinicians in proper prognosis of stroke. Ischemic Stroke Lesion Segmentation Challenge (ISLES22) provides data of stroke data for various stroke patients, the dataset consists of three modalities of data Fluid Attenuated Inversion Recovery (FLAIR), Apparent Diffusion Coefficient (ADC) and Diffusion-Weighted Imaging (DWI). Multimodal data gives a comprehensive understanding of the brain and the stroke lesions. Complex algorithms and processing steps are required to ensure that the data is prepared for further processing. The objective of this experimental research is to create a novel multimodal preprocessing model that can be used for the preprocessing of the multimodal data from various MRI modalities (FLAIR, DWI and ADC). The proposed model supports the automatic removal of artefacts from the multimodal data, by identifying and applying the best preprocessing techniques for Image Registration (Affine or non-rigid transformations), Normalization (Z Score or min-max normalizations), Denoising Techniques (Gaussian, Median, Non-Local Means, or Anisotropic Diffusion filters) and Bias Field correction. The best technique is identified using the evaluation techniques of Dice Coefficient, Signal-to-Noise Ratio (SNR), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM) and Root Mean Squared Error (RMSE). Preprocessing is critical process to improve the outcome of the subsequent analysis including segmentation. Here, we propose an Enhanced Image Registration and Artefact Correction (EIRAC) model with Best Image Registration Technique (BIRT) and Multiple Orientation Normalization Denoising and Bias field correction Parallelly (MONDBP) algorithms for the preprocessing of multimodal MRI images to provides better results for the segmentation of stroke lesions through Machine Learning models. 2025, Binghamton University Libraries. All rights reserved.

Source

Northeast Journal of Complex Systems;Volume;7;Issue;1;Article No.;4;

Date

01-01-2025

Publisher

Binghamton University Libraries

Subject

Complex Processing; Machine Learning; Magnetic Resonance Imaging; Multimodal Preprocessing; Preprocessing; Preprocessing Model; Stroke

Coverage

Joseph A., CHRIST University, Bangalore, India; Chandra J., CHRIST University, Bangalore, India

Rights

All Open Access; Gold Open Access

Relation

ISSN: 25778439;

Format

online

Language

English

Type

Article

Identifier

https://doi.org/10.22191/nejcs/vol7/iss1/4

https://www.scopus.com/pages/publications/86000090114?origin=resultslist