Abstract: An image fusion method for medical applications, comprising: 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; e. performing a first delineation step on the actual radiation region; 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 contour points around the actual radiation region in the second image; i. performing a second delineation step on the actual radiation region and go to step (f).

Abstract: A digital image processing method for automatically outlining a contour of a figure in a digital image, including: testing parameters of a region within the digital image according to a plurality of cascaded tests; determining whether the region contains characteristic features of the figure within the digital image; computing location parameters of the characteristic features in the region for the figure within the digital image; determining boundary parameters for the figure corresponding to the location parameters of the characteristic features in the region; computing an information map of the digital image; computing a set of indicative pixels for the contour of the figure; and automatically outlining the contour of the figure using the set of indicative pixels, the information map, and a contour outlining tool.

Abstract: A digital image processing method for multiple passes diagnostic alignment of in vivo images, comprising the steps of: acquiring images using an 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; and setting off alarm signals to a local site provided that suspected abnormalities have been identified for each pass forming a registration bundle; selecting identification elements of an image from the registration bundle of one pass; and retrieving corresponding images from another pass.

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: 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: 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 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.

Abstract: A digital image processing method detects facial features in a digital image. This method includes the steps of detecting iris pixels in the image, clustering the iris pixels, and selecting at least one of the following schemes to identify eye positions: applying geometric reasoning to detect eye positions using the iris pixel clusters; applying a summation of squared difference method using the iris pixel clusters to detect eye positions; and applying a summation of squared difference method to detect eye positions from the pixels in the image. The method applied to identify eye positions is selected on the basis of the number of iris pixel clusters, and the facial features are located using the identified eye positions.

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 method for rectifying misalignment in a stereoscopic display system (140) comprises: providing a pair of input images to an image processor (120); creating an image source displacement map for the pair of input images; obtaining a display displacement map (150); and applying the image source displacement map and the display displacement map to the pair of input images to create a rectified stereoscopic image pair.

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 computer vision/image processing method generates a depth map. In one embodiment, the present invention provides a method of forming a relative depth map from at least three x-ray images of a patient taken from different perspectives. The method includes the steps of: (a) selecting one of the x-ray images as a reference image; (b) identifying corresponding objects within the reference image and the other images; (c) computing a disparity between the identified corresponding objects; and (d) generating the relative depth map from the disparities.

Abstract: Deriving a three-dimensional panorama from a plurality of images of a scene generated by a range imaging camera of the type that produces ambiguities in range information includes the steps of: (a) acquiring a plurality of adjacent images of the scene, wherein there is an overlap region between the adjacent images and at least some of the adjacent images are range images; (b) providing offset data for the range images in order to recover corrected relative scene spatial information and provide a corrected range image, and (c) deriving a three-dimensional panorama from the corrected range image. In order to provide offset data, a relative range difference is detected between adjacent range images as a constant offset between the adjacent images; and the constant offset is applied to at least one of adjacent range images to correct for ambiguities in the relative ranges of the range images.

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 images that belong to one or more collections that have at least the threshold value of undesirable 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 includes the steps of: determining a digitally reconstructed radiograph from the planning image; identifying a region of the target's projection in the digitally reconstructed radiograph; capturing a radiographic image corresponding to the digitally reconstructed radiograph; identifying a region in the captured radiographic image; comparing the region of the target's projection in the digitally reconstructed radiograph with the identified region in the captured radiographic image; and determining a delivery of the radiation therapy in response to this comparison.

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 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: 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 digital image processing method for determining an orientation of a face in a digital color image generates a mean grid pattern element image from a plurality of sample face images and an integral image from the digital color image. A face is located in the color digital image by using the integral image to perform a correlation test between the mean grid pattern element image and the digital color image at a plurality of effective resolutions by reducing the digital color image to a plurality of grid pattern element images at different effective resolutions and correlating the mean grid pattern element image with the plurality of grid pattern element images, whereby either the mean grid pattern element image or the grid pattern element images are provided at a plurality of different orientations. Accordingly, an orientation of the face in the color digital image is determined by using the images with different orientations in the correlation test.

Abstract: A digital image processing method for locating faces in a digital color image includes the steps of: generating a mean grid pattern element (MGPe) image from a plurality of sample face images; generating an integral image from the digital color image; and locating faces in the color digital image by using the integral image to perform a correlation between the mean grid pattern element (MGPe) image and the digital color image at a plurality of effective resolutions by reducing the digital color image to grid pattern element images (GPes) at different effective resolutions and correlating the MGPe with the GPes.