10542 results about "Image segmentation" patented technology

Systems and methods for image segmentation in generating computer models of a joint to undergo arthroplasty are disclosed. Some embodiments may include a method of partitioning an image of a bone into a plurality of regions, where the method may include obtaining a plurality of volumetric image slices of the bone, generating a plurality of spline curves associated with the bone, verifying that at least one of the plurality of spline curves follow a surface of the bone, and creating a 3D mesh representation based upon the at least one of the plurality of spline curve.

A computer based system and method of visualizing a region using multiple image data sets is provided. The method includes acquiring first volumetric image data of a region and acquiring at least second volumetric image data of the region. The first image data is generally selected such that the structural features of the region are readily visualized. At least one control point is determined in the region using an identifiable structural characteristic discernable in the first volumetric image data. The at least one control point is also located in the at least second image data of the region such that the first image data and the at least second image data can be registered to one another using the at least one control point. Once the image data sets are registered, the registered first image data and at least second image data can be fused into a common display data set. The multiple image data sets have different and complimentary information to differentiate the structures and the functions in the region such that image segmentation algorithms and user interactive editing tools can be applied to obtain 3d spatial relations of the components in the region. Methods to correct spatial inhomogeneity in MR image data is also provided.

An automated image processing system and method are provided for class-based segmentation of a digital image. The method includes extracting a plurality of patches of an input image. For each patch, at least one feature is extracted. The feature may be a high level feature which is derived from the application of a generative model to a representation of low level feature(s) of the patch. For each patch, and for at least one object class from a set of object classes, a relevance score for the patch, based on the at least one feature, is computed. For at least some or all of the pixels of the image, a relevance score for the at least one object class based on the patch scores is computed. An object class is assigned to each of the pixels based on the computed relevance score for the at least one object class, allowing the image to be segmented and the segments labeled, based on object class.

Systems and methods are disclosed for segmenting a digital image to identify an object portrayed in the digital image from background pixels in the digital image. In particular, in one or more embodiments, the disclosed systems and methods use a first neural network and a second neural network to generate image information used to generate a segmentation mask that corresponds to the object portrayed in the digital image. Specifically, in one or more embodiments, the disclosed systems and methods optimize a fit between a mask boundary of the segmentation mask to edges of the object portrayed in the digital image to accurately segment the object within the digital image.

The invention provides a device and process for real-time screening of areas that can be identified as suspicious either through image segmentation utilizing image processing techniques or through treatment with an exogenous fluorescent marker that selectively localizes in abnormal areas. If screening detects a suspicious area, then the invention allows acquiring of autofluorescence images at multiple selected narrow differentiating spectral bands so that a "virtual biopsy" can be obtained to differentiate abnormal areas from normal areas based on differentiating portions of autofluorescence spectra. Full spatial information is collected, but autofluorescence data is collected only at the selected narrow spectral bands, avoiding the collection of full spectral data, so that the speed of analysis is increased.

A system, and computer implemented method are provided for interactively displaying three-dimensional structures. Three-dimensional volume data (34) is formed from a series of two-dimensional images (33) representing at least one physical property associated with the three-dimensional structure, such as a body organ having a lumen. A wire frame model of a selected region of interest is generated (38b). The wireframe model is then deformed or reshaped to more accurately represent the region of interest (40b). Vertices of the wire frame model may be grouped into regions having a characteristic indicating abnormal structure, such as a lesion. Finally, the deformed wire frame model may be rendered in an interactive three-dimensional display.

Two stereo images are created by splitting a single image into two images using a field of view dividing splitter. The two images can be displayed side by side so that they can be viewed directly using stereopticon technology, heads-up display, or other 3-D display technology, or they can be separated for individual eye viewing. The two images focus on one imager such that the right image appears on the right side of the monitor and the left image appears on the left side of the monitor. The view of the images is aimed to converge at a given object distance such that the views overlap 100% at the object distance. Measurement is done with at least one onscreen cursor.

A method is discussed of tracking objects in a series of n-D images (102) that have objects (106, 108) appearing in a background (110, 112), that method comprises using a probabilistic model of the appearance of the objects and of the appearance of the background in the images, and using an evaluation of whether particular pixels in the images (102) are a part of an object (106, 108) or a part of the background (110, 112), that evaluation comprising determining the posterior model probabilities that a particular pixel (x) or group of pixels belongs to an object or to the background, and further comprising marginalising over these object/background membership probabilities to yield a function of the pose parameters of the objects, where at least the object/background membership is adjudged to be a nuisance parameter and marginalised out.

The present invention is a diagnosis support system providing automated guidance to a user by automated retrieval of similar disease images and user feedback. High resolution standardized labeled and unlabeled, annotated and non-annotated images of diseased tissue in a database are clustered, preferably with expert feedback. An image retrieval application automatically computes image signatures for a query image and a representative image from each cluster, by segmenting the images into regions and extracting image features in the regions to produce feature vectors, and then comparing the feature vectors using a similarity measure. Preferably the features of the image signatures are extended beyond shape, color and texture of regions, by features specific to the disease. Optionally, the most discriminative features are used in creating the image signatures. A list of the most similar images is returned in response to a query. Keyword query is also supported.

The present invention generally relates to intravascular ultrasound (IVUS) image segmentation methods, and is more specifically concerned with an intravascular ultrasound image segmentation method for characterizing blood vessel vascular layers. The proposed image segmentation method for estimating boundaries of layers in a multi-layered vessel provides image data which represent a plurality of image elements of the multi-layered vessel. The method also determines a plurality of initial interfaces corresponding to regions of the image data to segment and further concurrently propagates the initial interfaces corresponding to the regions to segment. The method thereby allows to estimate the boundaries of the layers of the multi-layered vessel by propagating the initial interfaces using a fast marching model based on a probability function which describes at least one characteristic of the image elements.

An apparatus, system, method, and computer readable medium containing computer-executable code for implementing image analysis uses multivariate statistical analysis of sample images, and allows segmentation of the image into different groups or classes, depending on a correlation to one or more sample textures, or sample surface features. In one embodiment, the invention performs multivariate statistical analysis of ultrasound images, wherein a tumor may be characterized by segmenting viable tissue from necrotic tissue, allowing for more detailed in vivo analysis of tumor growth beyond simple dimensional measurements or univariate statistical analysis. Application of the apparatus and method may also be used for characterizing other types of samples having textured features including, for example, tumor angiogenesis biomarkers from Power Doppler.

A method and apparatus for finding correspondence between portions of two images that first subjects the two images to segmentation by weighted aggregation (10), then constructs directed acylic graphs (16,18) from the output of the segmentation by weighted aggregation to obtain hierarchical graphs of aggregates (20,22), and finally applies a maximally weighted subgraph isomorphism to the hierarchical graphs of aggregates to find matches between them (24). Two algorithms are described; one seeks a one-to-one matching between regions, and the other computes a soft matching, in which is an aggregate may have more than one corresponding aggregate. A method and apparatus for image segmentation based on motion cues. Motion provides a strong cue for segmentation. The method begins with local, ambiguous optical flow measurements. It uses a process of aggregation to resolve the ambiguities and reach reliable estimates of the motion. In addition, as the process of aggregation proceeds and larger aggregates are identified, it employs a progressively more complex model to describe the motion. In particular, the method proceeds by recovering translational motion at fine levels, through affine transformation at intermediate levels, to 3D motion (described by a fundamental matrix) at the coarsest levels. Finally, the method is integrated with a segmentation method that uses intensity cues. The utility of the method is demonstrated on both random dot and real motion sequences.

A system and method for determining image similarity. The method includes the steps of automatically providing perceptually significant features of main subject or background of a first image; automatically providing perceptually significant features of main subject or background of a second image; automatically comparing the perceptually significant features of the main subject or the background of the first image to the main subject or the background of the second image; and providing an output in response thereto. In the illustrative implementation, the features are provided by a number of belief levels, where the number of belief levels are preferably greater than two. The perceptually significant features include color, texture and/or shape. In the preferred embodiment, the main subject is indicated by a continuously valued belief map. The belief values of the main subject are determined by segmenting the image into regions of homogenous color and texture, computing at least one structure feature and at least one semantic feature for each region, and computing a belief value for all the pixels in the region using a Bayes net to combine the features. In an illustrative application, the inventive method is implemented in an image retrieval system. In this implementation, the inventive method automatically stores perceptually significant features of the main subject or background of a plurality of first images in a database to facilitate retrieval of a target image in response to an input or query image. Features corresponding to each of the plurality of stored images are automatically sequentially compared to similar features of the query image. Consequently, the present invention provides an automatic system and method for controlling the feature extraction, representation, and feature-based similarity retrieval strategies of a content-based image archival and retrieval system based on an analysis of main subject and background derived from a continuously valued main subject belief map.

Described is a system for rapid object detection combining structural information with bio-inspired attentional mechanisms. The system oversegments an input image into a set of superpixels, where each superpixel comprises a plurality of pixels. For each superpixel, a bounding box defining a region of the input image representing a detection hypothesis is determined. An average residual saliency (ARS) is calculated for the plurality of pixels belonging to each superpixel. Each detection hypothesis that is out of a range of a predetermined threshold value for object size is eliminated. Next, each remaining detection hypothesis having an ARS below a predetermined threshold value is eliminated. Then, color contrast is calculated for the region defined by the bounding box for each remaining detection hypothesis. Each detection hypothesis having a color contrast below a predetermined threshold is eliminated. Finally, the remaining detection hypotheses are output to a classifier for object recognition.

The present invention relates to a system for the 3-D monitoring and analysis of motion-related behavior of test subjects. The system comprises an actual camera, at least one virtual camera, a computer connected to the actual camera and the computer is preferably installed with software capable of capturing the stereo images associated with the 3-D motion-related behavior of test subjects as well as processing these acquired image frames for the 3-D motion parameters of the subjects. The system of the invention comprises hardware components as well as software components. The hardware components preferably comprise a hardware setup or configuration, a hardware-based noise elimination component, an automatic calibration device component, and a lab animal container component. The software components preferably comprise a software-based noise elimination component, a basic calibration component, an extended calibration component, a linear epipolar structure derivation component, a non-linear epipolar structure derivation component, an image segmentation component, an image correspondence detection component, a 3-D motion tracking component, a software-based target identification and tagging component, a 3-D reconstruction component, and a data post-processing component In a particularly preferred embodiment, the actual camera is a digital video camera, the virtual camera is the reflection of the actual camera in a planar reflective mirror. Therefore, the preferred system is a catadioptric stereo computer vision system.

Segmentation of foreground from background layers in an image may be provided by a segmentation process which may be based on one or more factors including motion, color, contrast, and the like. Color, motion, and optionally contrast information may be probabilistically fused to infer foreground and/or background layers accurately and efficiently. A likelihood of motion vs. non-motion may be automatically learned from training data and then fused with a contrast-sensitive color model. Segmentation may then be solved efficiently by an optimization algorithm such as a graph cut.

A system and methods for the efficient segmentation of globally optimal surfaces representing object boundaries in volumetric datasets is provided. An optical surface detection system and methods are provided that are capable of simultaneously detecting multiple interacting surfaces in which the optimality is controlled by the cost functions designed for individual surfaces and by several geometric constraints defining the surface smoothness and interrelations. The present invention includes surface segmentation using a layered graph-theoretic approach that optimally segments multiple interacting surfaces of a single object using a pre-segmentation step, after which the segmentation of all desired surfaces of the object are performed simultaneously in a single optimization process.

The invention provides a binocular vision obstacle detection method based on three-dimensional point cloud segmentation. The method comprises the steps of synchronously collecting two camera images of the same specification, conducting calibration and correction on a binocular camera, and calculating a three-dimensional point cloud segmentation threshold value; using a three-dimensional matching algorithm and three-dimensional reconstruction calculation for obtaining a three-dimensional point cloud, and conducting image segmentation on a reference map to obtain image blocks; automatically detecting the height of a road surface of the three-dimensional point cloud, and utilizing the three-dimensional point cloud segmentation threshold value for conducting segmentation to obtain a road surface point cloud, obstacle point clouds at different positions and unknown region point clouds; utilizing the point clouds obtained through segmentation for being combined with the segmented image blocks, determining the correctness of obstacles and the road surface, and determining position ranges of the obstacles, the road surface and unknown regions. According to the binocular vision obstacle detection method, the camera and the height of the road surface can be still detected under the complex environment, the three-dimensional segmentation threshold value is automatically estimated, the obstacle point clouds, the road surface point cloud and the unknown region point clouds can be obtained through segmentation, the color image segmentation technology is ended, color information is integrated, correctness of the obstacles and the road surface is determined, the position ranges of the obstacles, the road surface and the unknown regions are determined, the high-robustness obstacle detection is achieved, and the binocular vision obstacle detection method has higher reliability and practicability.

Systems and methods for image pattern recognition comprise digital image capture and encoding using vector quantization (“VQ”) of the image. A vocabulary of vectors is built by segmenting images into kernels and creating vectors corresponding to each kernel. Images are encoded by creating a vector index file having indices that point to the vectors stored in the vocabulary. The vector index file can be used to reconstruct an image by looking up vectors stored in the vocabulary. Pattern recognition of candidate regions of images can be accomplished by correlating image vectors to a pre-trained vocabulary of vector sets comprising vectors that correlate with particular image characteristics. In virtual microscopy, the systems and methods are suitable for rare-event finding, such as detection of micrometastasis clusters, tissue identification, such as locating regions of analysis for immunohistochemical assays, and rapid screening of tissue samples, such as histology sections arranged as tissue microarrays (TMAs).

An event clustering method uses foreground and background segmentation for clustering images from a group into similar events. Initially, each image is divided into a plurality of blocks, thereby providing block-based images. Utilizing a block-by-block comparison, each block-based image is segmented into a plurality of regions comprising at least a foreground and a background. One or more luminosity, color, position or size features are extracted from the regions and the extracted features are utilized to estimate and compare the similarity of the regions comprising the foreground and background in successive images in the group. Then, a measure of the total similarity between successive images is computed, thereby providing image distance between successive images, and event clusters are delimited from the image distances.

Disclosed are an optical navigation positioning system based on CT (computed tomography) registration results and a navigation method thereby. The system comprises a preoperative CT image guide input module, an image segmentation module, a body surface initial-registration module, a preoperative CT image and intraoperative two-dimensional ultrasound image module and an intraoperative navigation module. By combining virtual reality and intraoperative ultrasound, intraoperative positioning errors caused by factors such as breathing are compensated, and accordingly a target point for coronary artery bypass grafting is accurately positioned and navigated. Cardiac and coronary vessel tree in preoperative cardiac CT image data is manually segmented and reconstructed, an augmented virtual reality environment integrating endoscope and virtual endoscope is built by the aid of optical navigation apparatus and CT-ultrasound-based intraoperative registration error correction, and accordingly the target point of coronary artery bypass grafting is accurately positioned and navigated.