39 results about "Breast density" patented technology

Cone Beam Breast CT (CBBCT) is a three-dimensional breast imaging modality with high soft tissue contrast, high spatial resolution and no tissue overlap. CBBCT-based computer aided diagnosis (CBBCT-CAD) technology is a clinically useful tool for breast cancer detection and diagnosis that will help radiologists to make more efficient and accurate decisions. The CBBCT-CAD is able to: 1) use 3D algorithms for image artifact correction, mass and calcification detection and characterization, duct imaging and segmentation, vessel imaging and segmentation, and breast density measurement, 2) present composite information of the breast including mass and calcifications, duct structure, vascular structure and breast density to the radiologists to aid them in determining the probability of malignancy of a breast lesion.

A device and methods for performing a simulated CT biopsy on a region of interest on a patient. The device comprises a gantry (22) configured to mount an x-ray emitter (24) and CT detector (26) on opposing sides of the gantry, a motor (28) rotatably coupled to the gantry such that the gantry rotates horizontally about the region of interest, and a high resolution x-ray detector (172) positioned adjacent the CT detector in between the CT detector and the x-ray emitter.

The present invention relates to mammography. To provide breast density assessment with improved accuracy in the results, a method (100) is proposed for providing mammography information about an object of interest, the region of interest comprising a tissue structure, with the following steps: a) acquiring (110) first image data (112) with first image acquisition parameters (114), wherein the first image parameters are adapted to a first radiation spectrum of a dual energy mode; and wherein the first image acquisition is performed with a low X-ray dosage of a pre-scan; b) acquiring (116) second image data (118) with second image acquisition parameters (120), wherein the second image parameters are adapted to a second radiation spectrum of the dual energy mode, and wherein the second image acquisition is performed with a higher X-ray dosage than the first image acquisition, and wherein the second image acquisition is a Mammography scan; c) performing (122) a dual energy basis material decomposition (124) based on the first and second image data to generate decomposed basis material image data (126); and d) deriving (128) a density information (130) of the tissue structure of the region of interest from the decomposed basis material image data; and providing (132) the density information to a user.

A method for categorizing whole-breast density is disclosed. The method includes the steps of exposing breast tissue to an acoustic signal; measuring a distribution of an acoustic parameter by analyzing the acoustic signal; and obtaining a measure of whole-breast density from said measuring step. An apparatus is also disclosed.

Methods and systems for assessing the risk of a human female subject for developing breast cancer. In particular, the present disclosure relates to combining a first clinical risk assessment, a secondclinical assessment based at least on breast density, and a genetic risk assessment, to obtain an improved risk analysis.

An automated content-based image retrieval method, a system and a computer program product for the classification of breast density from mammographic imagery. Raw digital mammogram images taken of patients are initially pre-processed to remove noise and enhance contrast, then subjected to pectoral muscle segmentation to produce region of interest (ROI) images. The ROI images are then decomposed using non-negative matrix factorization (NMF), where a non-negative sparsity constraint and reconstruction quality measures are imposed on the extracted and retained first few NMF factors. Based on the retained NMF factors, kernel matrix-based support vector machines classify the mammogram images binomially or multinomially to breast density categories. Methods of assessing and comparing the NMF-based breast classification method to principal component analysis or PCA-based methods are also described, and the NMF-based method is found to achieve higher classification accuracy and better handling of invariance in the digital mammogram images because of its parts-based factorization.

A method for content-based image retrieval for the classification of breast density from mammographic imagery is described. The breast density is characterized through the Fisher linear discriminants (FLD) extracted from the Principal Component Analysis (PCA). Unlike PCA, the FLD provides a very discriminative representation of the mammographic images in terms of the breast density. Various exemplary methods, systems and computer program products are also disclosed.

A method for content-based image retrieval for the classification of breast density from mammographic imagery is described. The breast density is characterized through the Fisher linear discriminants (FLD) extracted from the Principal Component Analysis (PCA). Unlike PCA, the FLD provides a very discriminative representation of the mammographic images in terms of the breast density. Various exemplary methods, systems and computer program products are also disclosed.

The invention relates to a deep learning classification method for mammography images based on a lightweight neural network. The method uses an image classification algorithm based on deep learning to realize the breast density classification of mammography images, and uses a deep learning framework based on a lightweight neural network. The method of the invention significantly improves the adaptability on small-scale image data sets, further improves the accuracy and processing speed of mammary gland density classification, and can realize automatic mammary gland density classification of mammary gland molybdenum target images.