Dougherty, Geoffreyhttp://hdl.handle.net/10139/7402024-03-28T11:12:54Z2024-03-28T11:12:54ZRobust measures of three-dimensional vascular tortuosity based on the minimum curvature of approximating polynomial spline fits to the vessel mid-lineJohnson, Michael J.Johnson, Michael J.http://hdl.handle.net/10139/4192013-08-13T22:22:51Z2007-07-01T00:00:00ZRobust measures of three-dimensional vascular tortuosity based on the minimum curvature of approximating polynomial spline fits to the vessel mid-line; Robust measures of three-dimensional vascular tortuosity based on the minimum curvature of approximating polynomial spline fits to the vessel mid-line
Johnson, Michael J.; Johnson, Michael J.
The clinical recognition of abnormal vascular tortuosity is important in the diagnosis of many diseases. This paper presents a novel approach to the quantification of vascular tortuosity, using robust metrics based on unit speed parameterizations of three-dimensional (3D) curvature. The use of approximating polynomial spline-fitting obviates the need for arbitrary filtering of mid-line data which is necessary with other tortuosity indices. The metrics were tested using both two-dimensional and three-dimensional synthesized images that mimicked clinically significant pathologies: two of the three metrics were scale invariant, additive, and produced tortuosity values tailored to be independent of the resolution of the imaging system. Our methodology is designed to explicitly handle the challenge of noisy data, and is largely tolerant of the inaccuracies in the mid-line extraction. While all the proposed metrics are sensitive to gently curved vessels, the rms curvature of the smoothest path was more effective in recognizing abnormalities involving high-frequency coiling such as occurs in malignant tumors. We have also indicated how values from two projection images, such as acquired in biplane angiography, can be combined to give an accurate approximation of the 3D value of this metric. Our proposed methodology is well-suited to automated detection and measurement, which are a prerequisite for clinical implementation.; The clinical recognition of abnormal vascular tortuosity is important in the diagnosis of many diseases. This paper presents a novel approach to the quantification of vascular tortuosity, using robust metrics based on unit speed parameterizations of three-dimensional (3D) curvature. The use of approximating polynomial spline-fitting obviates the need for arbitrary filtering of mid-line data which is necessary with other tortuosity indices. The metrics were tested using both two-dimensional and three-dimensional synthesized images that mimicked clinically significant pathologies: two of the three metrics were scale invariant, additive, and produced tortuosity values tailored to be independent of the resolution of the imaging system. Our methodology is designed to explicitly handle the challenge of noisy data, and is largely tolerant of the inaccuracies in the mid-line extraction. While all the proposed metrics are sensitive to gently curved vessels, the rms curvature of the smoothest path was more effective in recognizing abnormalities involving high-frequency coiling such as occurs in malignant tumors. We have also indicated how values from two projection images, such as acquired in biplane angiography, can be combined to give an accurate approximation of the 3D value of this metric. Our proposed methodology is well-suited to automated detection and measurement, which are a prerequisite for clinical implementation.
2007-07-01T00:00:00ZLacunarity analysis of spatial pattern in CT images of vertebral trabecular bone for assessing osteoporosisHenebry, Geoffrey M.Henebry, Geoffrey M.http://hdl.handle.net/10139/2502013-08-13T22:22:53Z2002-03-01T00:00:00ZLacunarity analysis of spatial pattern in CT images of vertebral trabecular bone for assessing osteoporosis; Lacunarity analysis of spatial pattern in CT images of vertebral trabecular bone for assessing osteoporosis
Henebry, Geoffrey M.; Henebry, Geoffrey M.
The structural integrity of vertebral trabecular bone is determined by the continuity of its trabecular network and the size of the
holes comprising its marrow space, both of which determine the apparent size of the marrow spaces in a transaxial CT image. A
model-independent assessment of the trabeculation pattern was determined from the lacunarity of thresholded CT images. Using
test images of lumbar vertebrae from human cadavers, acquired at different slice thicknesses, we determined that both median
thresholding and local adaptive thresholding (using a 7×7 window) successfully segmented the grey-scale images. Lacunarity analysis
indicated a multifractal nature to the images, and a range of marrow space sizes with significant structure around 14–18 mm2.
Preliminary studies of in vivo images from a clinical CT scanner indicate that lacunarity analysis can follow the pattern of bone
loss in osteoporosis by monitoring the homogeneity of the marrow spaces, which is related to the connectivity of the trabecular
bone network and the marrow space sizes. Although the patient sample was small, derived parameters such as the maximum
deviation of the lacunarity from a neutral (fractal) model, and the maximum derivative of this deviation, seem to be sufficiently
sensitive to distinguish a range of bone conditions. Our results suggest that these parameters, used with bone mineral density values,
may have diagnostic value in characterizing osteoporosis and predicting fracture risk.; The structural integrity of vertebral trabecular bone is determined by the continuity of its trabecular network and the size of the
holes comprising its marrow space, both of which determine the apparent size of the marrow spaces in a transaxial CT image. A
model-independent assessment of the trabeculation pattern was determined from the lacunarity of thresholded CT images. Using
test images of lumbar vertebrae from human cadavers, acquired at different slice thicknesses, we determined that both median
thresholding and local adaptive thresholding (using a 7×7 window) successfully segmented the grey-scale images. Lacunarity analysis
indicated a multifractal nature to the images, and a range of marrow space sizes with significant structure around 14–18 mm2.
Preliminary studies of in vivo images from a clinical CT scanner indicate that lacunarity analysis can follow the pattern of bone
loss in osteoporosis by monitoring the homogeneity of the marrow spaces, which is related to the connectivity of the trabecular
bone network and the marrow space sizes. Although the patient sample was small, derived parameters such as the maximum
deviation of the lacunarity from a neutral (fractal) model, and the maximum derivative of this deviation, seem to be sufficiently
sensitive to distinguish a range of bone conditions. Our results suggest that these parameters, used with bone mineral density values,
may have diagnostic value in characterizing osteoporosis and predicting fracture risk.
2002-03-01T00:00:00Z