Abstract: A method for 3-D cephalometric analysis acquires reconstructed volume image data from a computed tomographic scan of a patient's head. The acquired volume image data simultaneously displays from at least a first 2-D view and a second 2-D view. For an anatomical feature of the head, an operator instruction positions a reference mark corresponding to the feature on either the first or the second displayed 2-D view and the reference mark displays on each of the at least first and second displayed 2-D views. In at least the first and second displayed 2-D views, one or more connecting lines display between two or more of the positioned reference marks. One or more cephalometric parameters are derived according to the positioned reference marks, the derived parameters are displayed.

Abstract: A method for displaying a paranasal sinus region of a patient is executed at least in part on a computer, acquiring volume image data of the paranasal sinus region of the patient, identifying one or more airways within the paranasal sinus region from the volume image data, displaying the at least one or more airways, and highlighting one or more portions of the displayed airways that are constricted below a predetermined value.

Abstract: A method for analyzing a subject tooth. The method includes obtaining volume image data including at least the subject tooth and segments the subject tooth from within the volume data according to one or more operator instructions. An index is generated that is indicative of a suspected fracture or lesion identified for the segmented subject tooth. The subject tooth is displayed with the suspected fracture or lesion highlighted. The generated index also displays.

Abstract: A method for forming a panoramic image from a computed tomography image volume, acquires image data elements for one or more computed tomographic volume images of a subject, identifies a subset of the acquired computed tomographic images that contain one or more features of interest and defines, from the subset of the acquired computed tomographic images, a sub-volume having a curved shape that includes one or more of the contained features of interest. The curved shape is unfolded by defining a set of unfold lines wherein each unfold line extends at least between two curved surfaces of the curved shape sub-volume and re-aligning the image data elements within the curved shape sub-volume according to a re-alignment of the unfold lines. One or more views of the unfolded sub-volume are displayed.

Abstract: A method of providing a corrected reconstructed computed tomography image accesses image data for computed tomography images of a subject, identifying a subset of the computed tomography images that contain high density features. At least one high density feature is detected in each of the identified subset. The high density feature is classified and a compensation image is formed by distributing pixels representative of tissue over the classified high density feature. A difference sinogram is generated for each image in the identified subset of images by subtracting a first sinogram of the high density feature from a second sinogram of the original image. A resultant sinogram is generated for each image in the identified subset by adding a third sinogram generated according to the compensation image to the difference sinogram. The corrected reconstructed computed tomography image is formed according to the resultant sinogram generated for each image in the identified subset of images.

Abstract: A method and apparatus for generating a color mapping for a dental object. The method includes generating a transformation matrix according to a set of spectral reflectance data for a statistically valid sampling of teeth. Illumination is directed toward the dental object over at least a first, a second, and a third wavelength band, one wavelength band at a time. For each of a plurality of pixels in an imaging array, an image data value is obtained, corresponding to each of the at least first, second, and third wavelength bands. The transformation matrix is applied to form the color mapping by generating a set of visual color values for each of the plurality of pixels according to the obtained image data values and according to image data values obtained from a reference object at the at least first, second, and third wavelength bands. The color mapping can be stored in an electronic memory.

Abstract: A method for segmenting a feature of interest from a volume image acquires image data elements from the image of a subject. At least one view of the acquired volume is displayed. One or more boundary points along a boundary of the feature of interest are identified according to one or more geometric primitives defined by a user with reference to the displayed view. A foreground seed curve defined according to the one or more identified boundary points and a background seed curve encompassing and spaced apart from the foreground seed curve are formed. Segmentation is applied to the volume image according to foreground values that are spatially bounded within the foreground seed curve and according to background values that lie outside the background seed curve. An image of the segmented feature of interest is displayed.

Abstract: A method and apparatus for generating a color mapping for a dental object. The method includes generating a transformation matrix according to a set of spectral reflectance data for a statistically valid sampling of teeth. Illumination is directed toward the dental object over at least a first, a second, and a third wavelength band, one wavelength band at a time. For each of a plurality of pixels in an imaging array, an image data value is obtained, corresponding to each of the at least first, second, and third wavelength bands. The transformation matrix is applied to form the color mapping by generating a set of visual color values for each of the plurality of pixels according to the obtained image data values and according to image data values obtained from a reference object at the at least first, second, and third wavelength bands. The color mapping can be stored in an electronic memory.

Abstract: A method of automatic tooth segmentation, the method executed at least in part on a computer system acquires volume image data for either or both upper and lower jaw regions of a patient and identifies image content for a specified jaw from the acquired volume image data. For the specified jaw, the method estimates average tooth height for teeth within the specified jaw, finds a jaw arch region, detects one or more separation curves between teeth in the jaw arch region, defines an individual tooth sub volume according to the estimated average tooth height and the detected separation curves, segments at least one tooth from within the defined sub-volume, and displays the at least one segmented tooth.

Abstract: A method and apparatus for generating a color mapping for a dental object. The method includes generating a transformation matrix according to a set of spectral reflectance data for a statistically valid sampling of teeth. Illumination is directed toward the dental object over at least a first, a second, and a third wavelength band, one wavelength band at a time. For each of a plurality of pixels in an imaging array, an image data value is obtained, corresponding to each of the at least first, second, and third wavelength bands. The transformation matrix is applied to form the color mapping by generating a set of visual color values for each of the plurality of pixels according to the obtained image data values and according to image data values obtained from a reference object at the at least first, second, and third wavelength bands. The color mapping can be stored in an electronic memory.

Abstract: A method for 3-D interactive examination of a subject tooth, executed at least in part by a computer, obtains volume image data containing at least the subject tooth and background content adjacent to the subject tooth and displays a first image from the volume data that shows at least the subject tooth and the background content. A portion of the background content in the first image is identified according to a first operator instruction. Tooth content for at least the subject tooth in the first image is identified according to a second operator instruction. At least the subject tooth is segmented from within the volume data according to the first and second operator instructions. The segmented subject tooth is then displayed.

Abstract: A method for providing a calculated volume data value for an organ of a patient acquires image data for a first volume image series and for at least a second volume image series for the patient and displays the first volume image series. The method responds to an instruction to generate the volume data value by identifying a seed point in the first volume image series, correlating the seed point to the second volume image series, segmenting the organ in at least the second volume image series according to the correlated seed point, and calculating the volume data value for the organ using at least the segmented second volume image series. The calculated volume data value can be displayed.

Abstract: A method for providing a calculated volume data value for an organ of a patient acquires image data for a first volume image series and for at least a second volume image series for the patient and displays the first volume image series. The method responds to an instruction to generate the volume data value by identifying a seed point in the first volume image series, correlating the seed point to the second volume image series, segmenting the organ in at least the second volume image series according to the correlated seed point, and calculating the volume data value for the organ using at least the segmented second volume image series. The calculated volume data value can be displayed.

Abstract: A method for forming a panoramic image from a computed tomography image volume, acquires image data elements for one or more computed tomographic volume images of a subject, identifies a subset of the acquired computed tomographic images that contain one or more features of interest and defines, from the subset of the acquired computed tomographic images, a sub-volume having a curved shape that includes one or more of the contained features of interest. The curved shape is unfolded by defining a set of unfold lines wherein each unfold line extends at least between two curved surfaces of the curved shape sub-volume and re-aligning the image data elements within the curved shape sub-volume according to a re-alignment of the unfold lines. One or more views of the unfolded sub-volume are displayed.

Abstract: A method for segmenting a feature of interest from a volume image acquires image data elements from the image of a subject. One or more boundary points along a boundary of the feature of interest are identified according to one or more geometric primitives with reference to the displayed view. A foreground seed curve is defined according to the one or more identified boundary points. A background field array that lies outside of, and is spaced from, the foreground seed curve by a predetermined distance, is defined. Segmentation is applied to the volume image according to foreground values obtained according to image data elements that are spatially bounded on or within the foreground seed curve and according to background field array values to create a segmented feature of interest.

Abstract: A method for segmenting a feature of interest from a volume image acquires image data elements from the image of a subject. At least one view of the acquired volume is displayed. One or more boundary points along a boundary of the feature of interest are identified according to one or more geometric primitives defined by a user with reference to the displayed view. A foreground seed curve defined according to the one or more identified boundary points and a background seed curve encompassing and spaced apart from the foreground seed curve are formed. Segmentation is applied to the volume image according to foreground values that are spatially bounded within the foreground seed curve and according to background values that lie outside the background seed curve. An image of the segmented feature of interest is displayed.

Abstract: A method for registering a first imaging detector to a surface projects a sequence of k images toward the surface, wherein k?4, wherein each of the k images has a pattern of lines that extend in a direction that is orthogonal to a movement direction. The pattern encodes an ordered sequence of labels, each label having k binary elements, such that, in the movement direction, any portion of the pattern that is k equal increments long encodes one label of the ordered sequence. The method obtains, for at least a first pixel in the first imaging detector, along at least one line that is parallel to the movement direction, a first sequence of k signal values indicative of the k binary elements of a first label from the ordered sequence of labels and correlates the at least the first pixel in the first imaging detector to the surface.

Abstract: A method for displaying a diagnostic image acquires the diagnostic digital image and applies one or more pattern recognition algorithms to the acquired diagnostic digital image, detecting at least one feature within the acquired diagnostic digital image. At least a portion of the acquired diagnostic digital image displays with a marking at the location of the at least one detected feature. At least one detected feature displays under a first set of image display settings for a first interval, then under at least a second set of image display settings for a second interval.

Abstract: A method for detecting a linear structure in a digital mammographic image, using a processor or computer at least in part, locates at least one microcalcification candidate cluster in the image data and extracts a first region of interest that encloses the at least one microcalcification candidate cluster. The first region of interest is processed to identify feature points that correspond to geometric structures in the first region of interest. A linear detection algorithm is applied by a repeated process that selects a line model from a predefined set of line models and analyzes the line model to determine whether a linear structure is present in the first region of interest.

Abstract: A method and a system are disclosed for labeling an anatomical point associated with a lesion in an organ such as a lung. The method includes: a segmentation of a vessel tree anatomical structure starting from an autonomously determined initial image point; labeling the vessel segments of the vessel tree segmentation with segment labels based on a priori anatomical knowledge, thereby creating an individualized anatomical model; receiving a user-specified image point having a location from a user and locating a nearby vessel structure; tracking along the vessel structure in a direction towards a root of a parent vessel tree until a prior labeled vessel segment is encountered in the anatomical model, and assigning the label of the encountered prior labeled vessel segment from the anatomical model as an anatomical location label of the user-specified image point.