Abstract: The present invention relates to training data sets and a system and method for generating training images especially those which are medical images. Especially disclosed is a method of training a machine learning model to recognize movement of a body part in an acquired medical image The machine learning model is trained by varying/modifying a blur convolution kernel constructed with pixels oriented in a direction of the movement; the method including determining at least one motion weighting factor corresponding to a motion time period when the body part is moving during acquisition of the medical image, and using the motion weighting factor to vary/modify the blur convolution kernel.

Abstract: A system and method, relates to the field of medical imaging and image translation. It relates, in particular, to means to translate a for-processing image to a for-presentation image that is manufacturer and modality agnostic. It is a system and method for learning a translation mapping between for-processing and for-presentation image pairs via a generative adversarial network (GAN) based deep learning system. The Generative Adversarial Network (GAN) comprises a first neural network as a generator and a second neural network as a discriminator configured to train one another to learn a translation mapping between sets of paired for-processing and for-presentation images.

Abstract: The present invention relates to a method and system for the transformation of raw mammograms to normalised presentation and where the pixel values are independent of imaging conditions. The performed method includes: contrast enhancement, for improved visibility of the breast tissue composition, whereby a region of the breast is segmented and a contrast-stretching algorithm applied to the segmented region to preferably create an enhanced raw image or mammogram; local ‘maximum’ transform, whereby a 2-dimensional first filter is designed to extract the maximum pixel value from a region of interest (ROI) to preferably create a local maximum image or map; ratio map derivation, whereby the pixel value of the ratio map measures a relative response of the said pixel to its local maximum thus capturing the difference between breast composition regardless of mammogram variations.

Abstract: A system and method are disclosed for quantitative evaluation of image acquisition quality within a field of view and derivation of associated qualitative metrics to inform interpretation. The medical image evaluation system comprises: a data input device to acquire data of medical images: and a data processor to perform an image segmentation on one or more of the medical image(s) of a body portion to delineate tissue(s) of interest from a surrounding region within a field of view. The data processor is configured to resolve any missing tissue which is any portion of the tissue(s) of interest missing from the image segmentation. The data processor is configured to quantify suitability of the medical images for interpretation by making an estimate of amount or location of the missing tissue. A data device is configured to advise a user of the quantified suitability of the medical images for interpretation.

Abstract: The present invention relates to the characterisation of a tissue environment within a medical image, for example a quantitative image of a breast. A method is presented of characterization of a tissue environment within a quantitative medical image of a breast to predict risk of benign or malignant lesions via ‘blinded’ associations. A method is also presented of characterization of a tissue environment within a quantitative medical image of a breast to predict risk of benign or malignant lesions, diagnostic biopsy outcomes or risk of lesion progression, via informed associations of a biopsy or lesion.

Abstract: The present invention relates to a system and method to embed meta data from an imaging and communications system whereby the meta data is combined with image data as an input to a deep learning network. An image classification learning network is disclosed which comprises: a means to input image data and meta data; and an embedding layer comprising learnable embedding weights to encode the meta data to provide a learned object, and a softmax layer to classify a combination of the image data and the learned object.

Abstract: The present invention relates to automatic calibration of gamma value. In particular, it relates to automatically setting gamma value for the pre-processing of medical images, whereby the input image is standardised for the purpose of training machine learning models. The method is useful for useful for processing purposes to ensure that features may be identified reliably and consistently across different images by the same image processing methods.

Abstract: The present invention relates to contrast-enhanced radiographic imaging, the quantification of contrast agent in tissue and the assessment of the radiographic image quality. The invention provides a radiographic system-agnostic method to assess tissue administered with a radio-opaque contrast agent. The method is a system-agnostic means to accurately quantify contrast agent content in normal tissue and in cancerous tissue from contrast-enhanced radiographic images, and to assess and verify image quality and the efficacy of a clinical assessment from these images.

Abstract: The present invention relates to training data sets and a system and method for generating training images especially those which are medical images. Especially disclosed is a method of training a machine learning model to recognize movement of a body part in an acquired medical image The machine learning model is trained by varying/modifying a blur convolution kernel constructed with pixels oriented in a direction of the movement; the method including determining at least one motion weighting factor corresponding to a motion time period when the body part is moving during acquisition of the medical image, and using the motion weighting factor to vary/modify the blur convolution kernel.

Abstract: The present invention relates to a method and system for the transformation of raw mammograms to normalised presentation and where the pixel values are independent of imaging conditions. The performed method includes: contrast enhancement, for improved visibility of the breast tissue composition, whereby a region of the breast is segmented and a contrast-stretching algorithm applied to the segmented region to preferably create an enhanced raw image or mammogram; local ‘maximum’ transform, whereby a 2-dimensional first filter is designed to extract the maximum pixel value from a region of interest (ROI) to preferably create a local maximum image or map; ratio map derivation, whereby the pixel value of the ratio map measures a relative response of the said pixel to its local maximum thus capturing the difference between breast composition regardless of mammogram variations.

Abstract: Use of tissue composition and anthropomorphic measures in a method for the detection and quantification of arterial calcification in an organ is described for disease risk prediction and stratification. A radiographic image of an organ is transformed quantitatively to a tissue composition map indicating a total amount of organ tissue; a calcification map is generated indicating position in the tissue composition map of calcified tissue; calcification free tissue composition map is generated from the tissue composition map using the position of calcified tissue in the calcification map; a vessel map of the position of vessels in the tissue composition map is generated; and the vessel map is combined with the calcification map to generate a map of vessel calcification indicating the position of calcified vessels in the tissue composition map. Scores are based on arterial calcification in a breast which indicates disease in the breast and other organs.

Abstract: Use of tissue composition and anthropomorphic measures in a method for the detection and quantification of arterial calcification in an organ is described for disease risk prediction and stratification. A radiographic image of an organ is transformed quantitatively to a tissue composition map indicating a total amount of organ tissue; a calcification map is generated indicating position in the tissue composition map of calcified tissue; calcification free tissue composition map is generated from the tissue composition map using the position of calcified tissue in the calcification map; a vessel map of the position of vessels in the tissue composition map is generated; and the vessel map is combined with the calcification map to generate a map of vessel calcification indicating the position of calcified vessels in the tissue composition map. Scores are based on arterial calcification in a breast which indicates disease in the breast and other organs.

Abstract: The present invention relates to a system and apparatus comprising automated analysis and feedback on soft tissue imaging quality, including analysis and feedback in real time and in conformance with key parameters and metrics which affect the quality of an image. The system is arranged to implement a method wherein method for providing a qualitative and/or quantitative assessment of an image including several steps. The steps include using an imaging apparatus to obtain at least one soft tissue image of a patient and at least one test object image; deriving a soft tissue image parameter and statistical data from image data from the soft tissue image. The system and method are characterised by employing a metric for the assessment in comparison to the parameter and statistical data, wherein the metric is obtained from the soft tissue image data, data obtained from the at least one test object image, and data from the imaging apparatus.

Abstract: A system and method for validating the accuracy of image parameters, especially for images used in the medical field. The system and method may be used for validating a native parameter from a source image of a source object, wherein: one or more native parameters from the source image is analysed with a reference data to determine whether the native parameter(s) is/are plausible.

Abstract: A method comprises receiving image data representative of an X-ray image of at least part of a subject comprising tissue, wherein a first part of the X-ray image is directly 5 exposed and a second part of the X-ray image is representative of tissue in a region of the subject; and determining a measure of attenuation in dependence on an energy measure obtained from the first part of the X-ray image and an energy measure obtained from the second part of the X-ray image.

Abstract: The present invention relates to a system and apparatus comprising automated analysis and feedback on soft tissue imaging quality, including analysis and feedback in real time and in conformance with key parameters and metrics which affect the quality of an image. The system is arranged to implement a method wherein method for providing a qualitative and/or quantitative assessment of an image including several steps. The steps include using an imaging apparatus to obtain at least one soft tissue image of a patient and at least one test object image; deriving a soft tissue image parameter and statistical data from image data from the soft tissue image. The system and method are characterised by employing a metric for the assessment in comparison to the parameter and statistical data, wherein the metric is obtained from the soft tissue image data, data obtained from the at least one test object image, and data from the imaging apparatus.

Abstract: A method comprises receiving image data representative of an X-ray image of at least part of a subject comprising tissue, wherein a first part of the X-ray image is directly 5 exposed and a second part of the X-ray image is representative of tissue in a region of the subject; and determining a measure of attenuation in dependence on an energy measure obtained from the first part of the X-ray image and an energy measure obtained from the second part of the X-ray image.

Abstract: The present invention relates to a method of reconstruction of an object from projections, more particularly to a quantitative reconstruction of an object from projection views of the object. For example, quantitative reconstruction of an image of a human breast from projection views generated by digital breast tomosynthesis (DBT), computed tomography (CT), or standard mammography, and use of the reconstruction to identify densest regions.

Abstract: The present invention relates to an imaging technique and an imaging system and more particularly to an automatic assessment of quantitative measure(s)/properties(s) of an object wherein, for example, an imaging system is used to capture an image, following which image properties are quantified using image processing techniques. An imaging technique obtains an image of an object along a first axis, or by way of a first technique, and subsequently obtains an image of the object along a second axis, or by way of a second technique. One or more pixels from the first image are selected and designated as reference pixels. An automated comparison between corresponding regions of the first and second images are then performed which is based upon the reference pixels, so as to indicate regions of interest.

Abstract: The present invention relates to a method of reconstruction of an object from projections, more particularly to a quantitative reconstruction of an object from projection views of the object. For example, quantitative reconstruction of an image of a human breast from projection views generated by digital breast tomosynthesis (DBT), computed tomography (CT), or standard mammography, and use of the reconstruction to identify densest regions.