The purpose of the thesis was to investigate the possibility of using machine learn-ing for automation of liver fat measurements in fat-water magnetic resonancei maging (MRI). The thesis presents methods for texture based liver classificationand Proton Density Fat Fraction (PDFF) regression using multi-layer perceptrons utilizing 2D and 3D textural image features. The first proposed method was a data classification method with the goal to distinguish between suitable andunsuitable regions to measure PDFF in. The second proposed method was a combined classification and regression method where the classification distinguishes between liver and non-liver tissue. The goal of the regression model was to predict the difference d = pdff mean − pdff ROI between the manual ground truth mean and the fat fraction of the active Region of Interest (ROI).Tests were performed on varying sizes of Image Feature Regions (froi) and combinations of image features on both of the proposed methods. The tests showed that 3D measurements using image features from discrete wavelet transforms produced measurements similar to the manual fat measurements. The first method resulted in lower relative errors while the second method had a higher method agreement compared to manual measurements.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-151286 |
| Date | January 2018 |
| Creators | Grundström, Tobias |
| Publisher | Linköpings universitet, Datorseende |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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