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 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: Computerized detection and diagnostic schemes for sonographic images combine the benefits of computerized machine detection with the acquisition of non-radiographic medical images of special use for the screening of high risk, young patients who do not want the effects of ionizing characteristic of mammography. The lesion schemes employ computer-assisted interpretation of medical sonographic images, and output potential lesion sites and/or diagnosis of those lesions. More specifically, an embodiment of the computerized detection scheme involves convoluting a sonographic image with a mask of a given ROI (region of interest) size, and calculating a skewness value for each mask location, and assembling the calculated skewness values to form a skewness image. Thresholds are applied to pixels of the skewness image to determine potential shadows.

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.