Abstract: Embodiments relate to finding similar coronary angiograms in a database of coronary angiograms. An aspect includes receiving angiography data for a coronary artery, processing the angiography data to identify one or more semantic features of the coronary artery, and identifying one or more nearest coronary angiograms for each of the one or more semantic features from the database of coronary angiograms. The method also includes receiving a disease attribute associated with each of the one or more nearest coronary angiograms.

Abstract: Embodiments relate to segmenting blood vessels in angiogram images. An aspect includes a method that includes receiving and preprocessing at least one angiogram frame and preprocessing. In one embodiment, at least one angiogram frame is received and preprocessed. Bottom-up filtering of the preprocessed angiogram frame and top-down segmentation of the preprocessed angiogram frame are performed based on the results of the bottom-up filtering. The bottom-up filtering and the top-down segmentation are iteratively repeated until the difference between results of the top-down segmentation from consecutive iterations is equal to or below a threshold value. Based on determining that a difference between results of the top-down segmentation from consecutive iterations is below or equal to the threshold value, the results of the top-down segmentation are outputted.

Abstract: Embodiments relate to segmenting blood vessels in angiogram images. An aspect includes a method that includes receiving and preprocessing at least one angiogram frame and preprocessing. In one embodiment, at least one angiogram frame is received and preprocessed. Bottom-up filtering of the preprocessed angiogram frame and top-down segmentation of the preprocessed angiogram frame are performed based on the results of the bottom-up filtering. The bottom-up filtering and the top-down segmentation are iteratively repeated until the difference between results of the top-down segmentation from consecutive iterations is equal to or below a threshold value. Based on determining that a difference between results of the top-down segmentation from consecutive iterations is below or equal to the threshold value, the results of the top-down segmentation are outputted.

Abstract: Machine learning solutions compensate for data missing from input (training) data and thereby arrive at a predictive model that is based upon, and consistent with, the training data. The predictive model can be generated within a learning algorithm framework by transforming the training data to generate modality or similarity kernels. Similarity values can be generated for these missing similarity values.

Abstract: 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.

Abstract: Use of medical workflows where a first medical workflow is obtained from a plurality of medical acts performed in sequence that related to care of a patient. A set of condition-indication rules is applied to the first medical workflow to determine first condition information. The first condition information relates to a likelihood that a first medical condition exists in the patient.

Abstract: Embodiments relate to associating coronary angiography image annotations with SYNTAX score for assessment of coronary artery disease. Aspects include receiving and processing a plurality of angiogram videos from a coronary angiography study into a plurality of frames, selecting and displaying a key frame from the plurality of frames for each angiogram video in a browsing interface, and receiving a selection of one of the key frame from a user. Aspects further include displaying the angiogram video associated with the selected key frame in a video viewer interface, receiving a lesion annotation from the user for a frame of the angiogram video, and displaying a SYNTAX score questionnaire in the video viewer interface. Aspects further include annotating the frame of the angiogram video with the answers to the SYNTAX score questionnaire from the user and saving the answers to the SYNTAX score questionnaire with the annotated frame in a database.

Abstract: 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.

Abstract: Embodiments relate to finding similar coronary angiograms in a database of coronary angiograms. An aspect includes receiving angiography data for a coronary artery, processing the angiography data to identify one or more semantic features of the coronary artery, and identifying one or more nearest coronary angiograms for each of the one or more semantic features from the database of coronary angiograms. The method also includes receiving a disease attribute associated with each of the one or more nearest coronary angiograms.

Abstract: Embodiments relate to segmenting blood vessels in angiogram images. An aspect includes a method that includes receiving and preprocessing at least one angiogram frame and preprocessing. In one embodiment, at least one angiogram frame is received and preprocessed. Bottom-up filtering of the preprocessed angiogram frame and top-down segmentation of the preprocessed angiogram frame are performed based on the results of the bottom-up filtering. The bottom-up filtering and the top-down segmentation are iteratively repeated until the difference between results of the top-down segmentation from consecutive iterations is equal to or below a threshold value. Based on determining that a difference between results of the top-down segmentation from consecutive iterations is below or equal to the threshold value, the results of the top-down segmentation are outputted.

Abstract: Embodiments relate to segmenting blood vessels in angiogram images. An aspect includes a method that includes receiving and preprocessing at least one angiogram frame and preprocessing. In one embodiment, at least one angiogram frame is received and preprocessed. Bottom-up filtering of the preprocessed angiogram frame and top-down segmentation of the preprocessed angiogram frame are performed based on the results of the bottom-up filtering. The bottom-up filtering and the top-down segmentation are iteratively repeated until the difference between results of the top-down segmentation from consecutive iterations is equal to or below a threshold value. Based on determining that a difference between results of the top-down segmentation from consecutive iterations is below or equal to the threshold value, the results of the top-down segmentation are outputted.

Abstract: According to one embodiment of the present invention, a method for echocardiogram view classification is provided. According to one embodiment of the present invention, a method comprises: obtaining a plurality of video images of a subject; aligning the plurality images; using the aligned images to generate a motion magnitude image; filtering the motion magnitude image using an edge map on image intensity; detecting features on the motion magnitude image, retaining only those features which lie in the neighborhood of intensity edges; encoding the remaining features by generating, x, y image coordinates, a motion magnitude histogram in a window around the feature point, and a histogram of intensity values near the feature point; and using the encoded features to classify the video images of the subject into a predetermined classification.

Abstract: 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.

Abstract: 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.

Abstract: A visual media de-identification system is described. The system includes an image merger and a de-identifying engine. The image merger is configured to merge a sequence of images from a set of visual media data into an averaged image. The de-identifying engine is configured to: bound portions of the averaged image that are determined to be relatively fixed, wherein each bounded portion is identified by a corresponding position in the averaged image; generate a template comprising the bounded portions and the corresponding position for each bounded portion in the averaged image; and de-identify the sequence of images by obfuscating content in the bounded portions.

Abstract: A method for de-identification of visual media data, including: merging a sequence of images from a set of visual media data into an averaged image; bounding portions of the averaged image that are determined to be relatively fixed, wherein each bounded portion is identified by a corresponding position in the averaged image; generating a template comprising the bounded portions and the corresponding position for each bounded portion in the averaged image; and de-identifying the sequence of images by obfuscating content in the bounded portions.

Abstract: Synthesis of novel images from example images is achieved by determining a pixelwise optical flow among example images, computing a parameter set for a new image, and synthesizing the new image based on the parameter vector and the example images. The parameter set may describe characteristics of the image, in which case the characteristics are applied to a neural network trained with the example images in order to synthesize the novel image. The parameter set may also be an estimate of relative contributions of each of the example images to the new image, in which case the new image may be synthesized by taking a linear combination of the example images, weighted by the parameter set. In one embodiment, both the example and the new images are of the same object. In another embodiment, the example image represents a first object while the new image represents a second object. In yet another embodiment, a set of target images is synthesized from a single target image and a set of source images.