39 results about "Breast density" patented technology

A mammography system using a tissue exposure control relying on estimates of the thickness of the compressed and immobilized breast and of breast density to automatically derive one or more technic factors. The system further uses a tomosynthesis arrangement that maintains the focus of an anti-scatter grid on the x-ray source and also maintains the field of view of the x-ray receptor. Finally, the system finds an outline that forms a reduced field of view that still encompasses the breast in the image, and uses for further processing, transmission or archival storage the data within said reduced field of view.

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 are provided for the noninvasive measurement of the subepithelial impedance of the breast and for assessing the risk that a substantially asymptomatic female patient will develop or be at substantially increased risk of developing proliferative or pre-cancerous changes in the breast, or may be at subsequent risk for the development of pre-cancerous or cancerous changes. A plurality of electrodes are used to measure subepithelial impedance of parenchymal breast tissue of a patient at one or more locations and at least one frequency, particularly moderately high frequencies. The risk of developing breast cancer is assessed according to measured and expected or estimated values of subepithelial impedance for the patient and according to one or more experienced-based algorithms. Devices for practicing the disclosed methods are also provided.

A method and apparatus configured to analyze breast density based on magnetic resonance imaging (MRI) of a breast of a patient includes the steps of segmenting an MR image of the breast from one set of three-dimensional breast MRI images, and analyzing the amount of dense tissue and the morphological distribution of the dense tissue and a data processor configured by software to perform these steps. Analyzing the amount of dense tissue and the morphological distribution of the dense tissue includes the steps of segmenting tissue data to separate breast tissue from other body tissue, separating tissue data of the dense and fatty tissues in the breast, and analyzing the morphological distribution of dense tissue in the breast to derive one or more three dimensional morphological parameters of the dense tissue distribution.

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 of treatment comprises administering 4-hydroxy tamoxifen percutaneously to a patient having dense breast tissue. The 4-hydroxy tamoxifen may be formulated in a hydroalcoholic gel or an alcoholic solution.

The invention provides a method for acquiring breast density, a device and a readable storage medium. The method comprises: a breast segmentation result image and a gland segmentation result image areobtained according to an inputted to-be-detected breast image and a preset segmentation model, wherein the preset segmentation model is obtained by carrying out machine learning by using a breast training image and a gland training image; a ratio of a gland volume value to a breast volume value is calculated according to the breast segmentation result image and the gland segmentation result image; and on the basis of the ratio, the breast density of the to-be-detected breast image is determined. According to the invention, with the segmentation model obtained by performing machine learning training on the breast training image and gland training image, the accuracy of the calculated breast density is improved and thus illness risk prediction of the breast cancer becomes accurate and visual; and because the breast segmentation result image and the gland segmentation result image are automatically acquired by the computer device, the calculation efficiency is improved.

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.

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.

The invention relates to a breast density classification method and system, a readable storage medium and computer equipment, belonging to the technical field of medical imaging. The method comprisesthe following steps of obtaining a breast image to be detected, carrying out segmentation processing on the mammary gland image by utilizing a preset mammary gland segmentation model; obtaining a mammary gland segmentation result through a preset mammary gland density classification model, enabling mammary glands in a mammary gland image to be independent, mapping the mammary gland segmentation result to the mammary gland image, highlighting the mammary glands on the target image, and classifying the mammary gland density in the target image through the preset mammary gland density classification model to obtain a mammary gland density classification result. The breast image is processed through the two models, mammary gland segmentation is firstly carried out, the processed mammary glandimage is mapped to the mammary gland image, the mammary gland area is highlighted for classification, the model recognition can reduce the discrimination error of the mammary gland image compared withmanual recognition, and the accuracy of mammary gland density classification of the mammary gland image is greatly improved.

A method and apparatus configured to analyze breast density based on magnetic resonance imaging (MRI) of a breast of a patient includes the steps of segmenting an MR image of the breast from one set of three-dimensional breast MRI images, and analyzing the amount of dense tissue and the morphological distribution of the dense tissue and a data processor configured by software to perform these steps. Analyzing the amount of dense tissue and the morphological distribution of the dense tissue includes the steps of segmenting tissue data to separate breast tissue from other body tissue, separating tissue data of the dense and fatty tissues in the breast, and analyzing the morphological distribution of dense tissue in the breast to derive one or more three dimensional morphological parameters of the dense tissue distribution.

Methods and treatments are taught for the treatment of breast disorders, including proliferative breast disease, breast cancer, and increased breast density. The methods and compositions deliver efficacious formulations of chemical and / or biological treatment medicaments to the breast via a transpapillary route.

Methods and systems are provided for the noninvasive measurement of the subepithelial impedance of the breast and for assessing the risk that a substantially asymptomatic female patient will develop or be at substantially increased risk of developing proliferative or pre-cancerous changes in the breast, or may be at subsequent risk for the development of pre-cancerous or cancerous changes. A plurality of electrodes are used to measure subepithelial impedance of parenchymal breast tissue of a patient at one or more locations and at least one frequency, particularly moderately high frequencies. The risk of developing breast cancer is assessed according to measured and expected or estimated values of subepithelial impedance for the patient and according to one or more experienced-based algorithms. Devices for practicing the disclosed methods are also provided.

The invention discloses an ultrasonic image preprocessing system and method for diagnosing breast lumps, and the method comprises the following steps: S1, extracting a gland area of a breast image, and S2, obtaining gland distribution features of the gland region; S3, determining the mammary gland density of a mammary gland image according to the proportion of the gland area in the foreground partof the mammary gland image; grading the breast density; S4, determining the image type of the mammary gland image in combination with the mammary gland density and the gland distribution characteristics; S5, determining a preset mammary gland image processing mode corresponding to the image type, and processing the mammary gland image by using the mammary gland image processing mode; S6, extracting a gland region of the mammary gland image; S7, determining the subareas of which the ratios are greater than a preset value as gland boundary areas, and calculating a segmentation threshold of eachgland boundary area; and S8, taking the average value of segmentation thresholds of gland boundary areas as a segmentation gland area threshold of the mammary gland image, and segmenting the gland boundary areas of the mammary gland image to obtain gland areas.

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.

The invention relates to a medical image enhancement method and a computer readable storage medium. The method comprises the following steps: inputting an acquired mammary gland grayscale image into an image segmentation network to obtain a breast region segmentation result and a gland region segmentation result; calculating the area proportion of the gland region in the breast region according tothe breast region segmentation result and the gland region segmentation result; obtaining a breast density classification result through the area ratio; and enhancing the mammary gland grayscale image based on the grayscale information of the mammary gland grayscale image and the breast density classification result to obtain an enhanced mammary gland image. According to the method, mammary glandgrayscale images are classified; different enhancement strategies are applied to images with different breast densities, so that focus areas in enhanced mammary gland images obtained from different mammary gland grayscale images can be more obvious, the focus areas can be detected more easily in the focus detection process, and the focus detection rate of different types of mammary gland grayscale images is increased.

The invention provides an automatic breast density grading method and device. The method comprises: acquiring a target breast X-ray image; preprocessing the target mammary gland X-ray image; and taking the preprocessed target mammary gland X-ray image as a prediction sample, inputting the prediction sample into a preset target depth residual error network, and taking the output of the target depthresidual error network as a mammary gland density grading result corresponding to the target mammary gland X-ray image. The breast density classification method and device can effectively improve theaccuracy and efficiency of feature recognition of the mammary gland X-ray image, can effectively and reliably improve the high efficiency and intelligent degree of the classification process of the mammary gland density corresponding to the mammary gland X-ray image, and can effectively improve the accuracy of mammary gland density classification.

The embodiments relate to a method, an apparatus, and a computer program for evaluating an x-ray image of a breast produced during a mammography. In order to simplify the evaluation of such an x-ray image in respect of the breast density, a method is proposed to automatically determine the masking risk caused by the mammographically dense tissue and to use this for categorizing, describing, and / or representing the breast density.

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 breast density quantitative calculation method in breast cancer risk assessment. The method specifically comprises the following steps that an original image of a breast molybdenum target is preprocessed, and histogram equalization, Gaussian filtering and down-sampling are carried out; a breast area is segmented from the original image of the breast molybdenum target, and the outer edge line of the breast and the inner edge line of the breast muscle are detected; fuzzy C-means are used for carrying out unsupervised clustering on pixels in the breast area; features ofa cluster region are extracted, obtained clusters are merged and classified into glands and fat, and the clusters are subjected to dichotomy recognition; dichotomy is carried out on the breast area,and a linear discriminant LDA classifier is trained; the size of the glands in the breast area is calculated according to a classification result. According to the breast density quantitative calculation method, full-automatic segmentation of breast tissue in the breast can be realized, and the burden of an imaging doctor is greatly reduced while an objective result is given.

Various embodiments are described herein for methods, devices and systems that can be used to determine a breast density value from an input image of patient's breast. In one example embodiment, the breast density value is calculated by receiving an input image corresponding to the digital mammogram, removing metadata information from the input image to generate an intermediate image, generating a region of interest (ROI) image based on the intermediate image, extracting values for predictor variables based on the metadata information, the intermediate image and the ROI image, and calculating breast density based on the values of the predictor variables. The breast density value is calculated based on a breast density model.

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.

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.