Abstract: A method of bounding an anatomical object of interest in a 3-dimensional volume image includes displaying an image of at least a portion of the object, selecting a plurality of points in the displayed image, at least a first and second point of the plurality of points spanning the object, forming a non-rectilinear surface bounding the plurality of points, identifying a seed point within the surface and extracting a plurality of statistical values corresponding to image voxels disposed proximate the seed point, and classifying image voxels within the surface into a first class and a second class based on the plurality of statistical values.

Abstract: A method of collecting information regarding an anatomical object of interest includes displaying an image characterized by a first region and a second region, wherein the first and second regions are mutually exclusive and the object is displayed within the second region, selecting first and second points spanning the object in the displayed image, at least one of the points being within the first region, and extracting a plurality of statistical values from image voxels, lying on a line segment between the first and second points, that correspond to the object.

Abstract: A method of image segmentation includes receiving a set of voxels, segmenting the set of voxels into a foreground group and a background group, and classifying voxels of the foreground group as either lesion voxels or normal anatomy voxels. The method also includes blocking the normal anatomy voxels and performing a second segmentation on voxels of the background group and the lesion voxels, the second segmentation forming a stage two foreground group comprising the lesion voxels and a portion of the voxels of the background group. The method further includes classifying voxels of the stage two foreground group as either stage two lesion voxels or stage two normal anatomy voxels.

Abstract: An apparatus for obtaining 3-D surface contour image data of a tooth has a double telecentric optical system disposed to form an image of the surface of the tooth onto an image detector array. A focus adjustment mechanism is actuable to adjust the position of either or both the double telecentric optical system and the image detector array along an optical axis to each of a sequence of focus positions. A control logic processor is in control signal communication with the focus adjustment mechanism to adjust focus position, and is in image data communication with the image detector array for receiving image data obtained by the image detector array and with a memory for storing the received image data corresponding to each of the sequence of focus positions. The control logic processor is further responsive to stored instructions for computing 3-D surface contour image data from the image data.

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 digital image editing method includes receiving a three-dimensional volume image of an anatomical object of interest, wherein the volume image is characterized by first and second mutually exclusive segmentation classes. The method also includes deriving a two-dimensional slice image from the volume image, selecting a single point on the slice image within the second segmentation class, and defining a plane in response to the selection of the single point, the plane dividing the second segmentation class into a target portion corresponding to the object and a remainder portion.

Abstract: A method of microcalcification detection in a digital mammographic image identifies one or more potential microcalcification sites in the mammographic image according to spot clustering. Each of the one or more potential microcalcification sites is assigned either as a member of a positive candidate set or as a member of a rejected candidate set. Optionally at least one subsequent classifier process that selectively assigns zero or more members of the positive candidate set to the rejected candidate set is executed, according to results from the at least one subsequent classifier process. One or more members of the rejected candidate set are selected as a reclamation candidate set according to results from the initial and any subsequent classifier process. One or more members of the reclamation candidate set are assigned either back to the rejected candidate set or to the positive candidate set according to results from a reclamation classifier process.

Abstract: An apparatus for obtaining 3-D surface contour image data of a tooth has a double telecentric optical system disposed to form an image of the surface of the tooth onto an image detector array. A focus adjustment mechanism is actuable to adjust the position of either or both the double telecentric optical system and the image detector array along an optical axis to each of a sequence of focus positions. A control logic processor is in control signal communication with the focus adjustment mechanism to adjust focus position, and is in image data communication with the image detector array for receiving image data obtained by the image detector array and with a memory for storing the received image data corresponding to each of the sequence of focus positions. The control logic processor is further responsive to stored instructions for computing 3-D surface contour image data from the image data.

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 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 for image linear structure detection in medical imaging. The method includes locating microcalcification (mcc) candidate spots in a mammographic image; forming candidate clusters; assigning ranks to the candidate clusters; identifying linear structures in the neighborhood where the candidate clusters reside; and altering the ranks of the candidate clusters for which linear structures have been identified in the neighborhood.

Abstract: A method of segmenting a lesion (910) from normal anatomy in a 3-dimensional image comprising the steps of: receiving an initial set of voxels (520) that are contained within the lesion to be segmented; growing a region which includes the lesion from the initial set of voxels; identifying a second set of voxels (530) on a surface of the normal anatomy; determining a surface containing the second set of voxels which demarks a boundary (540) between the lesion and the normal anatomy; and classifying voxels which are part of the lesion.

Abstract: A method of adjusting exposure of a digital camera based on range information, including a digital camera capturing a first digital image at a selected exposure of a scene having objects; providing range information having two or more range values indicating the distance from the digital camera to objects in the scene; using the range information and pixel values of the captured digital image to determine an exposure adjustment amount for the selected exposure; and applying the exposure adjustment amount to the digital image to produce a second digital image with adjusted exposure.

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 of analyzing a lesion in a medical digital image using at least one point contained within a lesion to be analyzed includes propagating a wave-front surface from the point(s) for a plurality of steps; partitioning the wave-front surface into a plurality of wave-front parts wherein each wave-front part is associated with a different portion of the wave-front surface corresponding to a previous propagation step; and analyzing at least one feature associated with each wave-front part to classify anatomical structures associated with the lesion and normal anatomy within the medical digital image.

Abstract: A system for the detection of abnormalities in a medical image of a subject. The system includes an examination bundle, a learning engine, and a detecting engine. The examination bundle includes at least one medical image of the subject from a first modality and at least one medical image of the subject from a second modality. The learning engine is employed for determining the characteristics of abnormalities within the at least one medical image from the first modality and the at least one medical image from the second modality. The detecting engine is employed for detecting abnormalities within at least one of the medical images comprising the examination bundle.

Abstract: A method for linear structure detection in mammographic images, comprising: locating a plurality of microcalcification candidate clusters in digital mammographic images; extracting a region of interest that encloses each of the candidate clusters; processing the region of interest to generate feature points that reveal geometric properties in the region; applying a line detection algorithm to the feature points to produce a line model; and analyzing the line model to determine whether a true linear structure is present in the first region of interest.

Abstract: An image indexer for indexing a plurality of images that includes a first data structure for subsequent classification of the one or more images. The first data structure includes characteristics for at least one class. An image classifier classifies one or more individual images found in the plurality of images as classified images according to the first data structure. A second data structure performs subsequent clustering of the plurality of images, wherein the second data structure includes at least two sequential events in a set of known events. The classified images are clustered according to the second data structure, and a representative image is selected from each cluster of classified images.

Abstract: A method for selecting an emphasis image from a collection of images based on facial identification comprises the steps of: (a) obtaining a collection of digital images; (b) detecting image patterns indicative of the presence of one or more faces in the digital images, thereby identifying one or more detected faces for each image in which a face is detected; (c) recognizing one or more faces from the detected faces for each of the images in which a face is detected; and (d) scoring an image based on the relative frequency of occurrence of a recognized face within the collection of images, thereby producing an emphasis image characteristic of the most frequently occurring face in the collection of images.

Abstract: A method of microcalcification detection in a digital mammographic image identifies one or more potential microcalcification sites in the mammographic image according to spot clustering. Each of the one or more potential microcalcification sites is assigned either as a member of a positive candidate set or as a member of a rejected candidate set. Optionally at least one subsequent classifier process that selectively assigns zero or more members of the positive candidate set to the rejected candidate set is executed, according to results from the at least one subsequent classifier process. One or more members of the rejected candidate set are selected as a reclamation candidate set according to results from the initial and any subsequent classifier process. One or more members of the reclamation candidate set are assigned either back to the rejected candidate set or to the positive candidate set according to results from a reclamation classifier process.