摘要:
A method of generating a category model for classifying medical images. The method comprises providing a plurality of medical images each categorized as one of a plurality of categorized groups, generating an index of a plurality of visual words according to a distribution of a plurality of local descriptors in each the image, modeling a category model mapping a relation between each visual word and at least one of the categorized groups according to the index, and outputting the category model for facilitating the categorization of an image based on local descriptors thereof.
摘要:
Methods and systems for retrieving and processing medical diagnostic images are provided, comprising using picture analysis prioritization visualization and reporting system (“PAPVR system”) to determine whether each of one or more images from an image database or imaging device is of medical interest to a reviewing physician, determine whether one or more of the images is representative of the images, and provide the one or more images to a display and analysis system for review by a reviewing physician. The PAPVR system can provide the one or more images with a Key Image that is representative of the images. In addition, the PAPVR system can detect whether a patient suffers from a particular ailment, and provide a reviewing physician quantitative information that is relevant to the patient's condition.
摘要:
A method for generating an index of the text of a video image sequence is provided. The method includes the steps of determining the image text objects in each of a plurality of frames of the video image sequence; comparing the image text objects in each of the plurality of frames of the video image sequence to obtain a record of frame sequences having matching image text objects; extracting the content for each of the similar image text objects in text string format; and storing the text string for each image text object as a video text object in a retrievable medium.
摘要:
A method of generating a category model for classifying medical images. The method comprises providing a plurality of medical images each categorized as one of a plurality of categorized groups, generating an index of a plurality of visual words according to a distribution of a plurality of local descriptors in each the image, modeling a category model mapping a relation between each visual word and at least one of the categorized groups according to the index, and outputting the category model for facilitating the categorization of an image based on local descriptors thereof.
摘要:
Embodiments of the invention relate to automating image classification with respect to coronary vessels in an angiography sequence. Two primary elements are employed, including training and recognition. Training pertains to the pre-processing images and extracting salient features that characterize the appearance of coronary arteries under different viewpoints. Recognition pertains to extraction of features from a new image sequence and determining a classification boundary for the new image from previously classified and labeled image sequences.
摘要:
Embodiments of the invention relate to a method, system, and computer program product to automate image classification with respect to coronary vessels in an angiography sequence. Two primary elements are employed, including training and recognition. Training pertains to the pre-processing images and extracting salient features that characterize the appearance of coronary arteries under different viewpoints. Recognition pertains to extraction of features from a new image sequence and determining a classification boundary for the new image from previously classified and labeled image sequences.
摘要:
Embodiments of the invention relate to a method, system, and computer program product to automate image classification with respect to coronary vessels in an angiography sequence. Two primary elements are employed, including training and recognition. Training pertains to the pre-processing images and extracting salient features that characterize the appearance of coronary arteries under different viewpoints. Recognition pertains to extraction of features from a new image sequence and determining a classification boundary for the new image from previously classified and labeled image sequences.
摘要:
A technique of determining information about orientation and amount of orientation of an image. The orientations of the image are sampled, and the samples are used to reconstruct information from which all orientations can be obtained. The samples each include information about specific components of the image, and these samples are assembled into a feature vector. The feature vector is discrete Fourier transformed whereby its continuous orientation information can be obtained.
摘要:
Methods and systems for retrieving and processing medical diagnostic images are provided, comprising using picture analysis prioritization visualization and reporting system (“PAPVR system”) to determine whether each of one or more images from an image database or imaging device is of medical interest to a reviewing physician, determine whether one or more of the images is representative of the images, and provide the one or more images to a display and analysis system for review by a reviewing physician. The PAPVR system can provide the one or more images with a Key Image that is representative of the images. In addition, the PAPVR system can detect whether a patient suffers from a particular ailment, and provide a reviewing physician quantitative information that is relevant to the patient's condition.
摘要:
Embodiments of the invention relate to automating image classification with respect to coronary vessels in an angiography sequence. Two primary elements are employed, including training and recognition. Training pertains to the pre-processing images and extracting salient features that characterize the appearance of coronary arteries under different viewpoints. Recognition pertains to extraction of features from a new image sequence and determining a classification boundary for the new image from previously classified and labeled image sequences.