Abstract: A method to define a curved slab region of interest that includes vessels while maximally excluding surrounding soft tissue and bone is provided. The thickness of the curved slab is automatically adapted to the thickness of the vessel and follows the tortuous vessel(s) so that an increase in tortuousity does not result in a disproportionate increase in the region of interest required to enclose the vessel. A plurality of boundary pairs is determined in the view plane to define a vessel. Vessel-intensities are determined for each one of the boundary pairs. The boundary pairs with associated intensities define the view of the vessel in the projection plane. Context-intensity could be defined in the area surrounding the boundary pairs in the projection and/or transverse plane. The method also includes several steps that will result in a better isolation and removal of non-vessel structures and view of the vessel(s) and its(their) branches.

Abstract: A method to quantify the vascular irregularity of aortoiliac arteries is provided. Inner wall and/or outer wall outlines of a vessel of interest are determined. The cross sectional area is determined for the area outlined by each outline. Using this cross sectional area a shape is selected that has substantially the same area as the outline. Subsequently, the shape is fitted to the outline. In one aspect, the irregularity index is calculated as the ratio of the outline and the outline of the fitted shape. In another aspect, the irregularity index is calculated as the ratio of at least a part of the outline and the outline of the fitted shape that corresponds to the same part of the outline. The irregularity index is visualized using a color scheme, a range of numbers, or a set of labels.

Abstract: A method that allows the quantification of the true mass of a calcium fragment located along a vessel is provided. The method is independent of the level of arterial contrast enhancement, does not require protocol-specific or scanner-specific calibration scans, and allows a detailed analysis of calcium distribution patterns. For each identified calcium fragment, the average intensity and volume is determined as a function of a plurality of intensity thresholds. Using these determined values brightness volume products are calculated for each of the plurality of intensity thresholds. The mass of a calcium segment is subsequently obtained from the calculated brightness volume products extrapolated at zero intensity and reference calcium parameters. The mass and volume of the calcium fragments could be visualized with respect to a vessel in a computer display.

Abstract: A method to quantify the radial endoluminal irregularity of aortoiliac arteries is provided. Radial endoluminal outlines of a vessel of interest are determined. The cross sectional area is determined for the area outlined by each endoluminal outline. Using this cross sectional area a shape is selected that has substantially the same area as the endoluminal outline. Subsequently, the shape is fitted to the endoluminal outline. In one aspect, the irregularity index is calculated as the ratio of the endoluminal outline and the outline of the fitted shape. In another aspect, the irregularity index is calculated as the ratio of at least a part of the endoluminal outline and the outline of the fitted shape that corresponds to the same part of the endoluminal outline. The irregularity index is visualized using a color scheme, a range of numbers, or a set of labels.

Abstract: A method to define a curved slab region of interest that includes vessels while maximally excluding surrounding soft tissue and bone is provided. The thickness of the curved slab is automatically adapted to the thickness of the vessel and follows the tortuous vessel(s) so that an increase in tortuousity does not result in a disproportionate increase in the region of interest required to enclose the vessel. A plurality of boundary pairs is determined in the view plane to define a vessel. Vessel-intensities are determined for each one of the boundary pairs. The boundary pairs with associated intensities define the view of the vessel in the projection plane. Context-intensity could be defined in the area surrounding the boundary pairs in the projection and/or transverse plane. The method also includes several steps that will result in a better boundary outline and view of the vessel.

Abstract: A computer-implemented method for determining and characterizing, which portions or shapes of a medical image correspond to a shape of interest is provided. A candidate shape is obtained after which a visible surface is computed adjacent to this candidate shape. A visible surface includes one or more portions of the medical image that are visible by the candidate shape. Once the visible surface is determined, parameters of the visible surface are computed. Then the method further includes the step of determining whether the candidate shape corresponds to a shape of interest. The method further includes the step of computing features of the candidate shape and/or classifying the candidate shape. The advantage of the computer-implemented method is that it provides a high detection specificity, i.e. reducing false positives, without sacrificing sensitivity of the detection of a shape of interest.

Abstract: A method to define a curved slab region of interest that includes vessels while maximally excluding surrounding soft tissue and bone is provided. The thickness of the curved slab is automatically adapted to the thickness of the vessel and follows the tortuous vessel(s) so that an increase in tortuousity does not result in a disproportionate increase in the region of interest required to enclose the vessel. A plurality of boundary pairs is determined in the view plane to define a vessel. Vessel-intensities are determined for each one of the boundary pairs. The boundary pairs with associated intensities define the view of the vessel in the projection plane. Context-intensity could be defined in the area surrounding the boundary pairs in the projection and/or transverse plane. The method also includes several steps that will result in a better boundary outline and view of the vessel.

Abstract: A method to quantify the radial endoluminal irregularity of aortoiliac arteries is provided. Radial endoluminal outlines of a vessel of interest are determined. The cross sectional area is determined for the area outlined by each endoluminal outline. Using this cross sectional area a shape is selected that has substantially the same area as the endoluminal outline. Subsequently, the shape is fitted to the endoluminal outline. In one aspect, the irregularity index is calculated as the ratio of the endoluminal outline and the outline of the fitted shape. In another aspect, the irregularity index is calculated as the ratio of at least a part of the endoluminal outline and the outline of the fitted shape that corresponds to the same part of the endoluminal outline. The irregularity index is visualized using a color scheme, a range of numbers, or a set of labels.

Abstract: A computer-implemented method for automatically detecting shapes in a medical image is provided. The method is based on the concept that normals to a surface intersect or nearly intersect with neighboring normals depending on the curvature features of the surface. The method first locates a surface in a medical image after which normal vectors are generated to the located surface. Then the method identifies at least one intersection and/or near intersection of the normal vectors. The key idea is that the number of intersections identifies shapes such as potential malignant candidates. The method also includes the step of scaling normal vectors to provide additional robustness to the shape detection. The method eliminates viewing of large segments of images, thereby markedly shortening interpretation time and improving accuracy of detection. It also provides for an early detection of precancerous growths so that they can be removed before evolving into a frank malignancy.

Abstract: A computer-implemented method for determining and characterizing, which portions or shapes of a medical image correspond to a shape of interest is provided. A candidate shape is obtained after which a visible surface is computed adjacent to this candidate shape. A visible surface includes one or more portions of the medical image that are visible by the candidate shape. Once the visible surface is determined, parameters of the visible surface are computed. Then the method further includes the step of determining whether the candidate shape corresponds to a shape of interest. The method further includes the step of computing features of the candidate shape and/or classifying the candidate shape. The advantage of the computer-implemented method is that it provides a high detection specificity, i.e. reducing false positives, without sacrificing sensitivity of the detection of a shape of interest.