Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162884
Title: GUI toolkit development for neuroimaging data preparation
Authors: Lim, Gia Lim
Keywords: Science::Medicine::Computer applications
Engineering::Computer science and engineering
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Lim, G. L. (2022). GUI toolkit development for neuroimaging data preparation. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/162884
Abstract: The evolution of magnetic resonance imaging (MRI) techniques has allowed many studies to use techniques such as functional MRI (fMRI) for a non-invasive study of the brain. [1] Given the increasing pervasiveness of neurological diseases, more is required in neuroimaging technology and subsequent data analysis. [2] Image segmentation is a key stage in medical image analysis and is foundational to most clinical applications. When performing MRI analysis, image segmentation is performed to allow for measuring and visualizing the brain’s anatomical structures. Analysis of brain changes, delineating of pathological regions, and surgical planning and image-guided interventions are also reasons for image segmentation. [3] Before any analysis, pre-processing of MRI data is done to ensure quality in the neuro-imaging data that has been collected. Advancements in non-invasive brain imaging technologies have given researchers and clinicians data in great quantities and qualities. Breaking down these big and complicated MRI datasets has become a manual and arduous process for users, as vital information is extracted manually. This manual analysis is often time-consuming and prone to errors. [4] Additionally, existing tools rely on users having programming backgrounds, and the tool that the team at NTU currently uses does not come with a user-friendly Graphic User Interface (GUI). [5] This paper will discuss the steps of fMRI data pre-processing steps as well as the application – fMRIConverter, which I will be building to assist researchers in preparing increased quality data for better qualitative diagnosis.
URI: https://hdl.handle.net/10356/162884
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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