GUI toolkit development for neuroimaging data preparation
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 dat...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2022
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| Online Access: | https://hdl.handle.net/10356/162884 |
| _version_ | 1811692229570330624 |
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| author | Lim, Gia Lim |
| author2 | Ke Yiping, Kelly |
| author_facet | Ke Yiping, Kelly Lim, Gia Lim |
| author_sort | Lim, Gia Lim |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T06:32:28Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/162884 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:32:28Z |
| publishDate | 2022 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1628842022-11-11T07:15:12Z GUI toolkit development for neuroimaging data preparation Lim, Gia Lim Ke Yiping, Kelly School of Computer Science and Engineering ypke@ntu.edu.sg Science::Medicine::Computer applications Engineering::Computer science and engineering 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. Bachelor of Engineering (Computer Science) 2022-11-11T07:14:12Z 2022-11-11T07:14:12Z 2022 Final Year Project (FYP) 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 https://hdl.handle.net/10356/162884 en application/pdf Nanyang Technological University |
| spellingShingle | Science::Medicine::Computer applications Engineering::Computer science and engineering Lim, Gia Lim GUI toolkit development for neuroimaging data preparation |
| title | GUI toolkit development for neuroimaging data preparation |
| title_full | GUI toolkit development for neuroimaging data preparation |
| title_fullStr | GUI toolkit development for neuroimaging data preparation |
| title_full_unstemmed | GUI toolkit development for neuroimaging data preparation |
| title_short | GUI toolkit development for neuroimaging data preparation |
| title_sort | gui toolkit development for neuroimaging data preparation |
| topic | Science::Medicine::Computer applications Engineering::Computer science and engineering |
| url | https://hdl.handle.net/10356/162884 |
| work_keys_str_mv | AT limgialim guitoolkitdevelopmentforneuroimagingdatapreparation |