Abstract: A method and system are disclosed for generating a machine learning model for automatic classification of radiographic images acquired by various acquisition protocols. The method includes the steps of: providing a plurality of radiographic images, detecting and segmenting in each of the radiographic image at least one regions of interest (ROI) as reference ROI, measuring at least one radiomic feature per reference ROI, identifying valid reference ROIs based on the measured radiomics values, and clustering the measured radiomics values of valid reference ROIs into at least two reference clusters according to a set of characteristics of image acquisition. A method and system are disclosed for classifying radiographic images by applying a machine learning model generated for automatic classification of radiographic images.

Abstract: An image processing apparatus for computing a quantitative imaging biomarker (QIB) of disease severity from variations in texture-based features in a tomographic image, the apparatus including a first preprocessing module for normalizing the intensities in the tomographic image; a second identification module for identifying at least one organ of interest in the tomographic image; a third ROI selection module for identifying and selecting a plurality of target ROIs and reference ROIs representative respectively of abnormal and normal pathology in the organ(s) of interest; a fourth ROI assessment module for extracting a plurality of texture-based feature signatures from the target ROIs and the reference ROIs, wherein the feature signatures are generated from distributions of statistical attributes extracted from each ROI; a fifth biomarker assessment module for computing the distance between the target ROI signatures and the reference ROI signatures, wherein the biomarker of disease severity is a function of t

Abstract: A system and method for the automated classification of lesions in CT images of the chest between measurable and non-measurable lesions is disclosed. The method comprises the steps of identifying lesions in a CT image, performing repeated measurements of selected metrics on the identified lesions and selecting as measurable lesions those with a variability of less than a pre-defined limit of agreement. Then a training step is carried out relying on a variety of image related features extracted from the lesions. Finally, labeling of lesions according to their likelihood of being consistently measured is performed.