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 undesirable images within an image sequence, comprising the steps of receiving the image sequence; computing one or more associated image characteristics for a group of images, wherein the group of images includes at least one image from the image sequence; identifying any undesirable group of images based on the computed image characteristics; collecting undesirable groups of images into at least one collection; receiving a threshold value of a minimum number of undesirable images; and flagging as undesirable only images that belong to one or more collections that have at least the threshold value of undesirable images.

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 detecting tubing in a radiographic image of a patient, executed at least in part by a control logic processor, obtains radiographic image data for a patient and identifies a region of interest in the radiographic image. A gradient magnitude image of the region of interest is formed and analyzed to identify one or more linear features by defining a band lying substantially within the region of interest and having a center point and repeating a sequence with two or more iterations of assigning a rotation angle for the rotatable band about the center point and computing the ensemble average of gradient magnitude values along each of a plurality of lines extending within the rotatable band at the defined rotation angle, then computing relative magnitudes for the lines. The one or more identified linear features are evaluated according to the results of the ensemble average computing.

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: A system for identifying anatomical structure depicted in an in vivo image, that includes an examination bundlette having a captured in vivo image; and a gastrointestinal atlas that includes a list of individual anatomical structures and characterization data of the individual anatomical structures. A classification engine analyzes the examination bundlette and the gastrointestinal atlas to identify the anatomical structure depicted in the captured in vivo image.

Abstract: Methods are disclosed for (A) adjusting the physical, spatial orientation of an immobilized subject in a multi-modal imaging system so as substantially to reproduce or match the physical, spatial orientation of a reference subject, wherein the reference subject is either (a) the same or (b) a different subject, either (1) during a prior imaging session for a later imaging session, or, in the case where a plurality of subjects is imaged in one imaging session, (2) during a contemporaneous imaging session; and (B) adjusting the virtual, spatial orientation of an immobilized subject in a set of multi-modal images.

Abstract: A system is disclosed that is configured for microcalcifications (mcc) detecting by forming a plurality of true mcc clusters and a plurality of normal clusters, gathering spot and cluster features from said clusters, extracting linear structure features, and using said spot, cluster and linear structure features in mcc detector algorithm training.

Abstract: A digital image processing method for detecting faces in a digital color image comprises the steps of: providing a distributed face detection system having complementary classifiers, wherein the classifiers are complementary in a frequency domain; selecting classifier parameters for the complementary classifiers from a plurality of different parameter generating sources, at least one of which is controllable by human input; reconfiguring the complementary classifiers in the distributed face detection system according to the selected classifier parameters; and detecting faces using the distributed face detection system.

Abstract: A method for automatically collecting viewer statistics from one or more persons in a movie theater, the method including the steps of capturing an image of the one or more persons in the movie theater with an infrared camera; using a face recognition algorithm to determine persons present in the movie theater; and determining one or more categories from characteristics from persons present to compute the viewer statistics.

Abstract: A method is disclosed for verifying linear structures in a digital mammographic image, comprising providing a configurable linear structure verifier in mammography computer assisted diagnosis system; optionally using an microcalcification candidate cluster driven linear structure verification methodology; selecting parameters for the linear structure verifier from a plurality of different parameter generating sources, at least one of which is controllable by human input; configuring the verifier according to selected parameters; and verifying linear structure using cascade rules.

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 movie theater includes an infrared camera disposed in an auditorium of a movie theater which infrared camera captures an image of one or more persons in the movie theater; and an algorithm that determines the number of persons present in the movie theater.

Abstract: A method for radiation therapy with target recognition has a first target identification system for obtaining first target characteristics within a patient's body. A second target identification system obtains second target characteristics within the body aided by the first target characteristics. A computation means for calculation of three-dimensional coordinates of the target region with respect to a three-dimensional radiotherapy system uses the second target characteristics. Irradiation means for radiotherapy mode adaptation is used in response to the second target characteristics and the calculation of three-dimensional coordinates of the target region.

Abstract: A digital image processing method for a centralized multi-mode medical tongue image inspection system, comprising the steps of: forming at least one service unit (300) for performing tongue record (220) construction; forming a processing unit (320) for tongue record inspection (308); and providing communication links (330) between the service units and the processing unit.

Abstract: A method and apparatus for delivering therapeutic radiation (112) to a radiotherapy target (119) in a patient (114) includes a diagnostic X-ray source (124, 125) connected to a treatment couch (116), facing a first side of the patient. An imaging device (118) is connected to the treatment couch facing a second side of the patient. The diagnostic X-ray source and the imaging device move in lockstep with movement of the treatment couch. The patient is in a fixed position relative to the treatment couch.

Abstract: An image fusion method which is used for medical applications. The method includes: (a) acquiring a first image with a planned radiation region; (b) acquiring a second image with actual radiation region; (c) determining if user defined landmarks have been placed on the first and second images, if user defined landmarks are present go to step (d), if not go to step (e); (d) pre-transforming the first image or second image or both images; and (e). performing a first delineation step on the actual radiation region. The method further includes: (f) determining if the delineation is correct, if yes go to step (g), if not go to step (h); (g) fusing the first and second image and exit process; and (h) selecting multiple guide points around the actual radiation region in the second image, wherein the guide points are position adaptable points; which are placed near but not on the boundaries of the region; and (i) performing a second delineation step on the actual radiation region and go to step (f).

Abstract: A method for determining a location of an object in a radiographic image by segmentation of a region in the image comprises the steps of: determining a first image intensity that is characteristic of high image intensities in the region; determining a second image intensity that is characteristic of low image intensities outside of the region; and determining if a pixel is added to or removed from the region based on the similarity of the pixel's intensity to the first and second intensity.