Abstract: A method for identifying the presence or progression of hypoxic ischemic brain injury includes, for each subset of one or more subsets of a three-dimensional medical image of a head of a patient: (i) inputting said each subset into a machine-learning model, (ii) extracting one or more features or feature maps from the machine-learning model, and (iii) constructing, based on the one or more features or feature maps, one of a sequence of vectors. The sequence of vectors is then pooled to obtain a scan-level vector that is used to obtain a score indicating HIBI presence or progression in the patient. For example, the scan-level vector can be inputted into a pre-trained classifier that generates the score based on the scan-level vector. The machine-learning model may be a pre-trained conventional neural network or support vector machine.

Abstract: A method of analyzing cell populations includes receiving, by a transceiver of a computing device, an image of a tissue sample. The method also includes analyzing, by a processor of the computing device, the image of the tissue sample using image analysis. The image analysis parameters are determined by machine learning. The method also includes determining, by the processor and based on the analyzing, one or more cell features, such as shape, of a cell in the tissue sample. The method further includes identifying, by the processor, an interaction of the cell with an additional cell based at least in part on the shape of the cell.

Abstract: A method of analyzing cell populations includes receiving, by a transceiver of a computing device, an image of a tissue sample. The method also includes analyzing, by a processor of the computing device, the image of the tissue sample using image analysis. The image analysis parameters are determined by machine learning. The method also includes determining, by the processor and based on the analyzing, one or more cell features, such as shape, of a cell in the tissue sample. The method further includes identifying, by the processor, an interaction of the cell with an additional cell based at least in part on the shape of the cell.

Abstract: Computerized interpretation of medical images for quantitative analysis of multi-modality breast images including analysis of FFDM, 2D/3D ultrasound, MRI, or other breast imaging methods. Real-time characterization of tumors and background tissue, and calculation of image-based biomarkers is provided for breast cancer detection, diagnosis, prognosis, risk assessment, and therapy response. Analysis includes lesion segmentation, and extraction of relevant characteristics (textural/morphological/kinetic features) from lesion-based or voxel-based analyses. Combinations of characteristics in several classification tasks using artificial intelligence is provided. Output in terms of 1D, 2D or 3D distributions in which an unknown case is identified relative to calculations on known or unlabeled cases, which can go through a dimension-reduction technique.

Abstract: Computerized interpretation of medical images for quantitative analysis of multi-modality breast images including analysis of FFDM, 2D/3D ultrasound, MRI, or other breast imaging methods. Real-time characterization of tumors and background tissue, and calculation of image-based biomarkers is provided for breast cancer detection, diagnosis, prognosis, risk assessment, and therapy response. Analysis includes lesion segmentation, and extraction of relevant characteristics (textural/morphological/kinetic features) from lesion-based or voxel-based analyses. Combinations of characteristics in several classification tasks using artificial intelligence is provided. Output in terms of 1D, 2D or 3D distributions in which an unknown case is identified relative to calculations on known or unlabeled cases, which can go through a dimension-reduction technique.

Abstract: Computerized interpretation of medical images for quantitative analysis of multi-modality breast images including analysis of FFDM, 2D/3D ultrasound, MRI, or other breast imaging methods. Real-time characterization of tumors and background tissue, and calculation of image-based biomarkers is provided for breast cancer detection, diagnosis, prognosis, risk assessment, and therapy response. Analysis includes lesion segmentation, and extraction of relevant characteristics (textural/morphological/kinetic features) from lesion-based or voxel-based analyzes. Combinations of characteristics in several classification tasks using artificial intelligence is provided. Output in terms of 1D, 2D or 3D distributions in which an unknown case is identified relative to calculations on known or unlabeled cases, which can go through a dimension-reduction technique.

Abstract: Computerized interpretation of medical images for quantitative analysis of multi-modality breast images including analysis of FFDM, 2D/3D ultrasound, MRI, or other breast imaging methods. Real-time characterization of tumors and background tissue, and calculation of image-based biomarkers is provided for breast cancer detection, diagnosis, prognosis, risk assessment, and therapy response. Analysis includes lesion segmentation, and extraction of relevant characteristics (textural/morphological/kinetic features) from lesion-based or voxel-based analyses. Combinations of characteristics in several classification tasks using artificial intelligence is provided. Output in terms of 1D, 2D or 3D distributions in which an unknown case is identified relative to calculations on known or unlabeled cases, which can go through a dimension-reduction technique.

Abstract: A method, system, and computer software product for analyzing medical images, including obtaining image data representative of a plurality of medical images of the abnormality, each medical image corresponding to an image of the abnormality acquired at a different time relative to a time of administration of a contrast medium, each medical image including a predetermined number of voxels; partitioning each medical image into at least two groups based on the obtained image data, wherein each group corresponds to a subset of the predetermined number of voxels, and each group is associated with a temporal image pattern in the plurality of medical images; selecting, from among the temporal patterns, an enhancement temporal pattern as representative of the abnormality; and determining, based on the selected temporal pattern, a medical state of the abnormality.

Abstract: A computerized method, system and computer program for the computerized fractal-based analysis of a structure as presented in a pattern on a medical image. Image data is generated from the medical image and a region of interest is selected. The image data is digitized and analyzed to reveal fractal-based computer-generated features of a texture of the image data. Then a qualifier is applied to the computer-generated features to obtain fractal characteristics of the image data. A multi-fractal nature is observed for the texture of the region of interest. A marker for assessing a risk of a disease is yielded based on the multi-fractal nature of the texture.

Abstract: A method for computer-assisted interpretation of medical images that factor in characteristics of an individual performing the interpretation. The method automatically determines and/or incorporates prevalence-based computer analysis based on an estimated likelihood of a pathological state, e.g., a malignancy. A system implementing the method includes the calculation of features or other characteristics of images in a known database, calculation of features of an unknown case, calculation of the probability (or likelihood) of disease state, calculation of the modified computer output that includes the internal prevalence (or internal decision-making process) of the user (or group of users), and output of the result.

Abstract: An automated method for determining prognosis based on an analysis of abnormality (lesion) features and parenchymal features obtained from medical image data of a patient. The techniques include segmentation of lesions from radiographic images, extraction of lesion features, and a merging of the features (with and without clinical information) to yield as estimate of the prognosis for the specific case. An example is given for the prognosis of breast cancer lesions using mammographic data. A computerized image analysis system for assessing prognosis combines the computerized analysis of medical images of cancerous lesions with the training-based methods of assessing prognosis of a patient, using indicators such as lymph node involvement, presence of metastatic disease, local recurrence, and/or death. It is expected that use of such a system to assess the severity of the disease will aid in improved decision-making regarding treatment options.

Abstract: A method, system, and computer software product for correlating medical images, comprising: obtaining first image data representative of a first medical image including a first abnormality; obtaining second image data representative of a second medical image including a second abnormality; determining at least one feature value for each of the first and second abnormalities using the first and second image data; calculating, based on the determined feature values, a likelihood value indicative of a likelihood that the first and second abnormalities are a same abnormality; and outputting the determined likelihood value.

Abstract: A computer-aided diagnosis (CAD) scheme to aid in the detection, characterization, diagnosis, and/or assessment of normal and diseased states (including lesions and/or images). The scheme employs lesion features for characterizing the lesion and includes non-parametric classification, to aid in the development of CAD methods in a limited database scenario to distinguish between malignant and benign lesions. The non-parametric classification is robust to kernel size.

Abstract: A method, system and computer readable medium for an intelligent search display into which an automated computerized image analysis has been incorporated. Upon viewing an unknown mammographic case, the display shows both the computer classification output as well as images of lesions with known diagnoses (e.g., malignant vs. benign) and similar computer-extracted features. The similarity index used in the search can be chosen by the radiologist to be based on a single feature, multiple features, or on the computer estimate of the likelihood of malignancy. Specifically the system includes the calculation of features of images in a known database, calculation of features of an unknown case, calculation of a similarity index, display of the known cases along the probability distribution curves at which the unknown case exists.

Abstract: A method of calculating a disease assessment by analyzing a medical image, comprising (1) extracting at least one lesion feature value from the medical image; (2) extracting at least one risk feature value from the medical image; and (3) determining the disease assessment based on the at least one lesion feature value and the at least one risk feature value. The method employs lesion characterization for characterizing the lesion, and risk assessment based on the lesion's surroundings, i.e., the environment local and distal to the lesion. Computerized methods both characterize mammographic lesions and assess the breast parenchymal pattern on mammograms, resulting in improved characterization of lesions for specific subpopulations, combining the benefits of both techniques.

Abstract: A method, system and computer readable medium for an intelligent search display into which an automated computerized image analysis has been incorporated. Upon viewing an unknown mammographic case, the display shows both the computer classification output as well as images of lesions with known diagnoses (e.g., malignant vs. benign) and similar computer-extracted features. The similarity index used in the search can be chosen by the radiologist to be based on a single feature, multiple features, or on the computer estimate of the likelihood of malignancy. Specifically the system includes the calculation of features of images in a known database, calculation of features of an unknown case, calculation of a similarity index, display of the known cases along the probability distribution curves at which the unknown case exists.

Abstract: A method, system and computer readable medium for automated detection of lung nodules in computed tomography (CT) image scans, including generating two-dimensional segmented lung images by segmenting a plurality of two-dimensional CT image sections derived from the CT image scans; generating three-dimensional segmented lung volume images by combining the two-dimensional segmented lung images; determining three-dimensional lung nodule candidates from the three-dimensional segmented lung volume images, including, identifying structures within the three-dimensional segmented lung volume images that meet a volume criterion; deriving features from the lung nodule candidates; and detecting lung nodules by analyzing the features to eliminate false-positive nodule candidates from the nodule candidates.

Abstract: A method of detecting a candidate abnormality in a sonographic medical image, based on determining a radial gradient index (RGI) at plural pixels, producing an RGI image, thresholding the RGI image, determining a candidate abnormality based on the thresholding step, and locating a center point of the candidate abnormality. The candidate abnormality may be classified by segmenting the candidate abnormality, including determining average radial gradients (ARDs) in the sonographic medical image based on the center point, extracting plural features from the segmented candidate abnormality, and determining a likelihood of the candidate abnormality being an actual abnormality based on the extracted plural features.

Abstract: A computerized method, system and computer program for the computerized fractal-based analysis of a structure as presented in a pattern on a medical image. Image data is generated from the medical image and a region of interest is selected. The image data is digitized and analyzed to reveal fractal-based computer-generated features of a texture of the image data. Then a qualifier is applied to the computer-generated features to obtain fractal characteristics of the image data. A multi-fractal nature is observed for the texture of the region of interest. A marker for assessing a risk of a disease is yielded based on the multi-fractal nature of the texture.

Abstract: A method, system and computer readable medium for an intelligent search display into which an automated computerized image analysis has been incorporated. Upon viewing an unknown mammographic case, the display shows both the computer classification output as well as images of lesions with known diagnoses (e.g., malignant vs. benign) and similar computer-extracted features. The similarity index used in the search can be chosen by the radiologist to be based on a single feature, multiple features, or on the computer estimate of the likelihood of malignancy. Specifically the system includes the calculation of features of images in a known database, calculation of features of an unknown case, calculation of a similarity index, display of the known cases along the probability distribution curves at which the unknown case exists.