Abstract: A clip for securing metal plates in walls constructed using masonry blocks. The clip includes a plate portion and a block portion. The block portion has members that form an opening that fits over a member of a masonry block. The plate portion also has members that form an opening that receives and secures a metal plate. When the block portion is placed on the member of a masonry block, the clip holds a metal plate in a fixed position relative to the masonry block. The clip can include notches on an edge of one of the members to more securely hold the clip on the masonry block.

Abstract: The invention is for a pair of pinch-resistant magnetic massage wands that have handles with a magnet disposed in a central bore of each handle. The magnet is oriented along a centerline of the handle so that a first end of the magnet is contained within the handle and a second end of the magnet is protruding from a top ridge of the handle. Each end of the magnet corresponds to a pole of the magnet. The magnets are oriented in the handles so that the same poles of each magnet protrude from the handle top ridge. When the massage wands are brought in close proximity of one another, the magnets exert opposing forces on one another to prevent a massage therapist from inadvertently pinching a patient with the massage wands.

Abstract: A clip for securing metal plates in walls constructed using masonry blocks. The clip includes a plate portion and a block portion. The block portion has members that form an opening that fits over a member of a masonry block. The plate portion also has members that form an opening that receives and secures a metal plate. When the block portion is placed on the member of a masonry block, the clip holds a metal plate in a fixed position relative to the masonry block. One member of the block portion can be angled. The block portion may also include a gripping portion that is used to secure the clip to a masonry block. The plate portion may include a spacer to secure the metal plate.

Abstract: A cross-time and cross-modality inspection method for medical image diagnosis. A first set of medical images of a subject is accessed wherein the first set is captured at a first time period by a first modality. A second set of medical images of the subject is accessed, wherein the second set is captured at a second time period by a second modality. The first and second sets are each comprised of a plurality of medical image. Image registration is performed by mapping the plurality of medical images of the first and second sets to predetermined spatial coordinates. A cross-time image mapping is performed of the first and second sets. Means are provided for interactive cross-time medical image analysis.

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 cross-time inspection method for medical image diagnosis. A first set of medical images of a subject is accessed wherein the first set is captured at a first time period. A second set of medical images of the subject is accessed, wherein the second set is captured at a second time period. The first and second sets are each comprised of a plurality of medical image. Image registration is performed by mapping the plurality of medical images of the first and second sets to predetermined spatial coordinates. A cross-time image mapping is performed of the first and second sets. Means are provided for interactive cross-time medical image analysis.

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 of processing a digital image of a scene to locate a planar surface in the digital image, includes providing a range information including two or more range values indicating the distance of objects in the scene from a known reference frame; and using a planar model and the range information to locate one or more planar surfaces in the digital image.

Abstract: A method of detecting an object of interest having a known size in a digital image, includes providing a range information including two or more range values indicating the distance of objects in the scene from a known reference frame; detecting a candidate object of interest in the image; determining range values corresponding to the candidate object of interest and using these range values and the known size of the object of interest to classify the candidate object of interest.

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: 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 delivering radiation therapy to a patient using a three-dimensional planning image for radiation therapy of the patient wherein the planning image includes a radiation therapy target. The method includes the steps of: determining desired image capture conditions for the capture of at least one two-dimensional radiographic image of the radiation therapy target using the three-dimensional planning image; detecting a position of the radiation therapy target in the at least one captured two-dimensional radiographic image; and determining a delivery of the radiation therapy in response to the radiation therapy target's detected position in the at least one captured two-dimensional radiographic image.

Abstract: A pattern recognition method for automatic abnormal tissue detection and differentiation using contrast enhanced MRI images. The method includes the steps of acquiring a plurality of MRI breast image sets; aligning the plurality of MRI breast images with respect to spatial coordinates; differencing the plurality of MRI breast image sets with a reference MRI image set, producing a plurality of difference image sets; segmenting the plurality of difference image sets, producing a plurality of MRI breast images with segmented intensity pixels; applying dynamic system identification to the segmented intensity pixels, producing a plurality of dynamic system parameters; and classifying the plurality of system parameters augmented with other physical or non-physical factors into different classes.

Abstract: In a digital image registration method, a second digital image is transformed to be more like a first digital image using a modification function, resulting in a modified second digital image. A set of first features of each of the first and modified second digital images is determined. Zones are specified in each of the first and modified second digital images. A set of zonal features, of each of the zones is determined. Compound mutual information is computed from the first and zonal features. The modification function is altered. The transforming, determining, specifying, determining, computing, and altering are iterated until the compound mutual information is maximized.

Abstract: A method for determining the presence of a face from image data utilizes at least first and second algorithms. The first algorithm prescreens the image data, by determining a plurality of face candidates utilizing a pattern matching technique that identifies image windows likely to contain faces based on color and shape information. The second algorithm processes the face candidates determined by the first algorithm, and uses a posterior probability function classifier to determine the presence of the face.

Abstract: A method for reconstructing computerized tomographic (CT) images of an object, including: scanning the object with a CT imaging system to acquire views that include measured projections of the object. Additionally, the method applies an iterative algorithm to minimize errors between the measured projections and reprojections of a reconstructed CT image, wherein at each iteration, projection errors become smaller causing the reconstructed CT image to become further refined.

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: A digital image processing method for exposure adjustment of in vivo images that includes the steps of acquiring in vivo images; detecting any crease feature found in the in vivo images; preserving the detected crease feature; and adjusting exposure of the in vivo images with the detected crease feature preserved.

Abstract: A digital image processing method for real-time automatic abnormality notification of in vivo images and remote access of in vivo imaging system, comprising the steps of: acquiring multiple sets of images using multiple in vivo video camera systems; for each in vivo video camera system forming an in vivo video camera system examination bundlette; transmitting the examination bundlette to proximal in vitro computing device(s); processing the transmitted examination bundlette; automatically identifying abnormalities in the transmitted examination bundlette; setting off alarming signals locally provided that suspected abnormalities have been identified; receiving one or more unscheduled alarming messages from one or more endoscopic imaging systems randomly located; routing alarming messages to remote recipient(s); and executing one or more corresponding tasks in relation to the alarming messages.

Abstract: A digital image processing method for image registration, that acquires a reference intensity image and a floating intensity image that is to be registered; and preprocesses the reference and the floating images, before converting the preprocessed reference image to a vectorized reference image. Subsequently, the vectorized reference image is converted to a reference index image. Additional image processing includes spatially transforming the preprocessed floating image using a transformation matrix; converting the transformed floating image to a vectorized floating image; converting the vectorized floating image to a floating index image; and obtaining joint statistics of the index images.