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 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 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.

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: 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 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 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 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 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 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 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 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 for creating a message template used for embedding hidden messages, the method includes the steps of determining a message template performance metric comprising a dispersal measure having both a spatial domain function and a frequency domain function; developing a numerical optimization algorithm containing the message template performance metric as a basis for optimization; determining the message template geometric configuration comprising: (i) determining a message template capacity; (ii) determining a message template area; and applying the numerical optimization algorithm to the message template geometric configuration which results in an optimal message template.

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 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 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: A method for producing a cropped digital image, includes the steps of: providing a plurality of partially overlapping source digital images; providing a cropping aspect ratio L:H, the cropping aspect ratio being the ratio of the length to the height of the cropped digital image; providing a cropping criterion, the cropping criterion being a criterion for the size and location of the cropped digital image; combining the source digital images to form a composite digital image; selecting the cropping region of the composite digital image according to the cropping criterion, said cropping region being a rectangular region having aspect ratio L:H, and having size and location determined by the cropping criterion; and, cropping the composite digital image to the cropping region to form a cropped digital image.

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.