Osteoporosis, associated with reduced bone mineral density and structural degeneration, greatly increases the risk of fragility fracture. Magnetic resonance imaging (MRI) has been applied to central skeletal sites including the proximal femur due to its non-ionizing radiation. A major challenge of volumetric bone imaging of the hip is the selection of regions of interest (ROIs) for computation of regional bone measurements. To address this issue, an MRI-based active shape model (ASM) of the human proximal femur is applied to automatically generate ROIs. The challenge in developing the ASM for a complex three-dimensional (3-D) shape lies in determining a large number of anatomically consistent landmarks for a set of training shapes. This thesis proposes a new method of generating the proximal femur ASM, where two types of landmarks, namely fiducial and secondary landmarks, are used. The method consists of—(1) segmentation of the proximal femur bone volume, (2) smoothing the bone surface, (3) drawing fiducial landmark lines on training shapes, (4) drawing secondary landmarks on a reference shape, (5) landmark mesh generation on the reference shape using both fiducial and secondary landmarks, (6) generation of secondary landmarks on other training shapes using the correspondence of fiducial landmarks and an elastic deformation of the landmark mesh, (7) computation of the active shape model. A proximal femur ASM has been developed using hip MR scans of 45 post-menopausal women. The results of secondary landmark generation were visually satisfactory, and no topology violation or notable geometric distortion artifacts were observed. Performance of the method was examined in terms of shape representation errors in a leave-one-out test. The mean and standard deviation of leave-one-out shape representation errors were 0.34mm and 0.09mm respectively. The experimental results suggest that the framework of fiducial and secondary landmarks allows reliable computation of statistical shape models for complex 3-D anatomic structures.
| Identifer | oai:union.ndltd.org:uiowa.edu/oai:ir.uiowa.edu:etd-7681 |
| Date | 01 May 2018 |
| Creators | Zhang, Xiaoliu |
| Contributors | Saha, Punam K. |
| Publisher | University of Iowa |
| Source Sets | University of Iowa |
| Language | English |
| Detected Language | English |
| Type | thesis |
| Format | application/pdf |
| Source | Theses and Dissertations |
| Rights | Copyright © 2018 Xiaoliu Zhang |
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