| dc.description.abstract | Purpose: The purpose of the project was to develop a processing toolchain that takes MR brain images and their anatomical segmentations as input and produces image visualizations, data visualizations, and statistical analyses in a unified manner for the users who work with neuroimaging and quantitative brain anatomy, including pathological anatomy. A secondary aim was to package and release the software publicly as open source.
Theory: The purpose of data analysis is to inspect, clean, transform and model raw data in order to discover salient information which can be used to build knowledge. Image visualization is defined as the process of converting data, such as pixel values or voxel values, into two- or three-dimensional graphical representations. Data visualization presents data in a graphical format. In the field of quantitative brain anatomy, data analysis, image visualization, and data visualization take place in separate systems.
Integrating data analysis, image visualization, and data visualization in a platform makes it possible for users to analyze region-based MR brain images more efficiently.
Method: To develop a toolchain that meets practitioner’s needs, another master student and my supervisor were consulted. Functions were developed iteratively based on feedback. By combining the developed functions, a workflow for region-based MRI brain image analysis was designed. Documentation and examples for all functions were written using R Markdown. Flowcharts for developed functions were drawn.
Result: An R package implementing image visualization, data visualization and statistical analysis functions was developed, including functions and a web app for region-based analysis of MR brain images. Functions were combined into a toolchain; data analysis and image visualization were integrated on the same platform. A workflow for region-based MR brain images analysis was designed.
Conclusion: The workflow design, programmed functions, and unified analysis platform for MR brain images are unique and novel contributions achieved in this master’s project. | en_US |
