Accurate and automatic segmentation techniques that do not require any explicit prior model have been of high interest in the medical community. We propose a fully-automatic method for segmenting the femur from 3D Computed Tomography scans, based on the graph-cut segmentation framework and the bone boundary enhancement filter analyzing second-order local structures. The presented algorithm is evaluated in large-scale experiments, conducted on 197 CT volumes, and compared to other three automatic bone segmentation methods. Out of the four tested approaches, the proposed algorithm achieved most accurate results and segmented the femur correctly in 81% of the cases.

Call number: LD2668 .T4 1986 F72 / Master of Science / Clinical Sciences

We introduce a new way to accurately segment the 3D femur from pelvic CT scans. The femur is a difficult target for segmentation due to its proximity to the acetabulum, irregular shape and the varying thickness of its hardened outer shell. Atypical bone morphologies, such as the ones present in hips suffering from Femoral Acetabular Impingements (FAIs) can also provide additional challenges to segmentation. We overcome these difficulties by (a) dividing the femur into the femur head and body regions (b) analysis of the femur-head and neighbouring acetabulum’s composition (c) segmentations with two levels of detail – rough and fine contours. Segmentations of the CT volume are performed iteratively, on a slice-by-slice basis and contours are extracted using the morphological snake algorithm. Our methodology was designed to require little initialization from the user and to deftly handle the large variation in femur shapes, most notably from deformations attributed to cam-type FAIs. Our efforts are to provide physicians with a new tool that creates patient-specific and high-quality 3D femur models while requiring much less time and effort. We tested our methodology on a database of 20 CT volumes acquired at the Ottawa General Hospital during a study into FAIs. We selected 6 CT scans from the database, for a total of 12 femurs, considering wide inter-patient variations. Of the 6 patients, 4 had unilateral cam-type FAIs, 1 had a bilateral cam-type FAI and the last was from a control group. The femurs segmented with our method achieved an average volume overlap error of 2.71 ± 0.44% and an average symmetric surface distance of 0.28 ± 0.04 mm compared against the same, manually segmented femurs. These results are better than all comparable literature and accurate enough to be used to in the creation of patient-specific 3D models.

The purpose of this thesis is to examine the structural variation of the proximal femur in human skeletal samples. The goal was to investigate and further describe the morphology of the proximal femur through quantitative observation. Subsets of measurements were used to calculate platymeric indices, which demonstrate the presence or absence of platymeria in a population. Metric analyses of the femur were also used to study size and shape differences in populations for indications of sexual dimorphism or asymmetry. Finally, selected platymeric index measurements were observed for intraobserver error, to test the validity of the measures and how well the researcher performed them. Data examining the morphology of the proximal femur were collected from a prehistoric Albanian site, a late 18th century Albanian site, and from a documented modern, industrial skeletal collection. In this study, platymeria was identified in the proximal femur in the two pre-industrial Albanian samples for both sexes and side. The results varied when compared to the industrial collection, which was eurymeric in both sexes and side. The proximal femoral differences in side and sex of the preindustrial and industrial remains shed light on biomechanical investigations. Overall, results conclude that the use of platymeric indices allowed the researcher to successfully distinguish variations in the proximal femur among three populations. / Thesis (M.A.)--Wichita State University, College of Liberal Arts and Sciences, Dept. of Anthropology / "May 2006." / Includes bibliographic references (leaves 73-77).

We introduce a new way to accurately segment the 3D femur from pelvic CT scans. The femur is a difficult target for segmentation due to its proximity to the acetabulum, irregular shape and the varying thickness of its hardened outer shell. Atypical bone morphologies, such as the ones present in hips suffering from Femoral Acetabular Impingements (FAIs) can also provide additional challenges to segmentation. We overcome these difficulties by (a) dividing the femur into the femur head and body regions (b) analysis of the femur-head and neighbouring acetabulum’s composition (c) segmentations with two levels of detail – rough and fine contours. Segmentations of the CT volume are performed iteratively, on a slice-by-slice basis and contours are extracted using the morphological snake algorithm. Our methodology was designed to require little initialization from the user and to deftly handle the large variation in femur shapes, most notably from deformations attributed to cam-type FAIs. Our efforts are to provide physicians with a new tool that creates patient-specific and high-quality 3D femur models while requiring much less time and effort. We tested our methodology on a database of 20 CT volumes acquired at the Ottawa General Hospital during a study into FAIs. We selected 6 CT scans from the database, for a total of 12 femurs, considering wide inter-patient variations. Of the 6 patients, 4 had unilateral cam-type FAIs, 1 had a bilateral cam-type FAI and the last was from a control group. The femurs segmented with our method achieved an average volume overlap error of 2.71 ± 0.44% and an average symmetric surface distance of 0.28 ± 0.04 mm compared against the same, manually segmented femurs. These results are better than all comparable literature and accurate enough to be used to in the creation of patient-specific 3D models.

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published_or_final_version / Medical Sciences / Master / Master of Medical Sciences

We introduce a new way to accurately segment the 3D femur from pelvic CT scans. The femur is a difficult target for segmentation due to its proximity to the acetabulum, irregular shape and the varying thickness of its hardened outer shell. Atypical bone morphologies, such as the ones present in hips suffering from Femoral Acetabular Impingements (FAIs) can also provide additional challenges to segmentation. We overcome these difficulties by (a) dividing the femur into the femur head and body regions (b) analysis of the femur-head and neighbouring acetabulum’s composition (c) segmentations with two levels of detail – rough and fine contours. Segmentations of the CT volume are performed iteratively, on a slice-by-slice basis and contours are extracted using the morphological snake algorithm. Our methodology was designed to require little initialization from the user and to deftly handle the large variation in femur shapes, most notably from deformations attributed to cam-type FAIs. Our efforts are to provide physicians with a new tool that creates patient-specific and high-quality 3D femur models while requiring much less time and effort. We tested our methodology on a database of 20 CT volumes acquired at the Ottawa General Hospital during a study into FAIs. We selected 6 CT scans from the database, for a total of 12 femurs, considering wide inter-patient variations. Of the 6 patients, 4 had unilateral cam-type FAIs, 1 had a bilateral cam-type FAI and the last was from a control group. The femurs segmented with our method achieved an average volume overlap error of 2.71 ± 0.44% and an average symmetric surface distance of 0.28 ± 0.04 mm compared against the same, manually segmented femurs. These results are better than all comparable literature and accurate enough to be used to in the creation of patient-specific 3D models.