12 results about "Lung field" patented technology

The present disclosure relates to a calm expiration phase, forced vital capacity determination and diagnosis method, system and dynamic X-ray machine, and relates to the technical field of calm expiration phase, forced vital capacity determination and diagnosis. The calm expiration phase determination method comprises: performing lung field segmentation on dynamic multiple X-ray two-dimensional chest images in the breathing process to obtain corresponding multiple X-ray two-dimensional lung field mask images; and determining the calm expiration phase in the breathing process according to multiple lung field mask areas of the multiple X-ray two-dimensional lung field mask images and lung field mask area change rates corresponding to the multiple lung field mask areas. The present disclosure can realize calm expiration phase, forced vital capacity determination and diagnosis.

The invention relates to the technical field of medical image processing, and discloses a chronic obstructive pulmonary emphysema distribution quantitative method and system based on multi-scale segmentation, and the method comprises the steps: carrying out the anisotropic diffusion filtering noise reduction of a chest CT image; segmenting a lung field and removing a blood vessel bronchial structure by adopting a region growing algorithm; multi-scale image representation is constructed based on a Gaussian pyramid, an emphysema candidate area is identified in a coarse scale layer, and a boundary is accurately drawn by adopting a self-adaptive threshold value in a fine scale layer; extracting local texture features to distinguish the lobular central emphysema and the total lobular emphysema; dividing severity levels according to spatial aggregation characteristics and density gradient distribution, and calculating an air swelling volume ratio and a distribution heterogeneity index; the three-dimensional pseudo-color volume is used for drawing visualization, a structured quantitative report is generated, accurate segmentation and subtype classification of the emphysema area are achieved, and comprehensive quantitative analysis indexes are provided.

The application discloses a pneumoconiosis staging recognition method and system based on multi-modal artificial imaging feature fusion, and relates to the technical field of medical image recognition. The method comprises the following steps: standardizing and pre-processing an input chest X-ray image and extracting a lung field region; in the lung field region, detecting and counting micro nodules based on clinical imaging prior knowledge, and extracting micro nodule quantity features reflecting the quantity and spatial distribution characteristics of nodular lesions; calculating the first-order entropy features of the lung field region image to quantify the complexity of lung parenchyma texture, and extracting high-dimensional features representing gray heterogeneity and structure statistical characteristics by using an imaging feature analysis method. The multi-class artificial imaging features are standardized and fused in a unified feature space, and the fused features are input into a learning classification model for pneumoconiosis period recognition. The application improves the stability and interpretability of pneumoconiosis staging recognition through multi-modal artificial imaging feature fusion modeling.

The invention provides a lung image recognition method and system for clinical diagnosis of the respiratory medicine department. The method comprises the steps that a binary mask containing complete lung parenchyma is obtained from a lung image; segmenting a target lung field image from the lung image based on the binary mask, and extracting a texture saliency map from the target lung field image; performing pixel-level fusion on the texture saliency map and the target lung field image to obtain a lung feature image, and performing multi-resolution pyramid decomposition on the lung feature image to obtain a Gaussian pyramid layer and a Laplacian pyramid layer; direction gradient histogram features and local binary pattern features are extracted from the Gaussian pyramid layer and the Laplacian pyramid layer respectively, and then a multi-resolution joint feature vector is constructed; and inputting the multi-resolution joint feature vector into a pre-trained image analysis network, and positioning a focus area in the lung image. According to the technical scheme provided by the invention, the lesion area in the lung image can be identified under the coupling interference of the anatomical structure and the pathological features.

A dynamic image analysis apparatus including a hardware processor that: obtains a chest dynamic image obtained by dynamic radiographing through radiation; extracts a lung field region from the dynamic image; calculates a feature amount about a blood flow rate, based on the lung field region; and limits a value of the calculated feature amount about the blood flow rate.

The present application relates to the technical field of X-ray digital image processing, in particular to a rib suppression method for chest DR image. The method comprises the following steps: 1) obtaining a DR original image and a rib region mask; 2) performing gray suppression on the rib region mask to obtain a gray value after rib internal suppression and a rib edge region after gray suppression; 3) extracting the DR original image to obtain a rib edge image, and performing coordinate transformation to obtain a coordinate-transformed rib edge image; 4) performing gradient calculation in two directions respectively to obtain gradient images of the rib edge in the two directions; 5) performing rib edge gradient suppression on the gradient images of the rib edge to obtain gradient images after rib edge suppression; and 6) reconstructing a gray image and replacing the rib edge region after gray suppression, wherein the gray image is a soft tissue image after rib suppression. The present application retains the texture information of the lung field region and does not cause unnatural transition of the rib edge.

Provided is a diagnostic support program that is possible to display a movement of an area whose shape 5 changes for each respiratory element including all or part of expired air or inspired air. There are provided processing of acquiring a plurality of frame images from a database that stores images, processing of specifying of a cycle of a 10 respiratory element including all or part of expired air or inspired air based on pixels in a specific area in each of the frame images, processing of detecting a lung field based on the cycle of the specified respiratory element, processing of dividing the 15 detected lung field into a plurality of block areas and calculating a change in image in a block area in each of the frame images, processing of Fourier-transforming a change in image in each block area in each of the frame images, processing of extracting a spectrum in a fixed 20 band including a spectrum corresponding to the cycle of the respiratory element, out of a spectrum obtained after the Fourier-transforming, processing of performing inverse Fourier transform on the spectrum extracted from the fixed band, and processing of 25 displaying each of the images after performing the inverse Fourier transform, on a display. 20 24 26 70 70 30 N ov 2 02 4 3 0 N o v 2 0 2 4 5 2 0 2 4 2 6 7 0 7 0

This invention discloses a chest CT image processing system, including a data acquisition module, an image enhancement module, a multi-resolution fusion module, and a high-fidelity export module. This invention relates to the field of intelligent CT image processing technology, specifically a chest CT image processing system. This system performs intensity cropping and normalization on CT images, and combines logarithmic domain bias field regularization iterative correction to restore tissue contrast. First, it locates the lung field, refines the boundaries using variational segmentation, and uses mask-constrained nonlocal mean denoising within the lung to preserve small lesions. It then combines a Hessian matrix with an improved Frangi metric for structural enhancement, fusing the original image, denoised image, and enhanced image. Based on structural response and gradient, it constructs a voxel-level importance mapping, performs high- and low-resolution resampling, and seamlessly synthesizes the images according to weights, preserving high resolution and detail in key areas, improving the accuracy of lung field and lesion boundaries, while reducing computational and storage overhead and effectively avoiding artifacts.

The present invention relates to assisting scapula positioning in chest X-ray imaging. Provided is a system and related method for assisting subject positioning in chest X-ray imaging, wherein the system comprises an optical detection device (110), a user interface (130), and a processor (120), connected to the optical detection device (110) and the user interface (130). Thereby, the processor (120) is configured to receive an optical image signal of a rear view of the subject(S), determine a current positioning of a scapula of the subject(S) based on the optical image signal, wherein the current positioning of the scapula is assessed as to whether and / or to which extent it would overlap or would not overlap a lung field of the subject to be imaged and determine a feedback for positioning the subject(S) and / or its scapula based on the determined current positioning of the scapula. The user interface (130) is configured to provide the feedback for positioning the subject.

This disclosure relates to a method and apparatus, electronic device, and storage medium for determining lung field area and assessing lung development, and pertains to the field of DR image processing technology. Specifically, the method for determining lung field area includes: acquiring two-dimensional DR images of the left lung and / or the right lung at multiple time points during respiration; and determining the area of ​​the left lung and / or the right lung during respiration based on the two-dimensional DR images of the left lung and / or the right lung at the same time points. Embodiments of this disclosure can achieve lung field area determination and lung development assessment.

The application relates to a chest X-ray multi-target segmentation method, an electronic device and a storage medium, the method comprising the following steps: acquiring an annotated chest X-ray image; inputting the annotated chest X-ray image into a trained single-encoder-double-decoder multi-target segmentation network to obtain different target segmentation results; wherein the training steps of the multi-target segmentation network comprise the following steps: acquiring an annotated chest X-ray image dataset; inputting the image dataset into the single-encoder-double-decoder multi-target segmentation network for training to obtain the trained single-encoder-double-decoder multi-target segmentation network. The collaborative attention jump connection module in the network performs one screening on rib bone information and important information of lung fields, hearts and other targets, thereby effectively improving the segmentation performance of the rib bones; and the attention guide multi-scale feature selection module in the network further improves the segmentation performance of clavicles, posterior rib bones and lung fields.

The invention relates to the technical field of medical image analysis, in particular to a dynamic DR lung blood flow perfusion diagnosis system based on deep learning, which comprises a dynamic DR imaging module, the dynamic DR imaging module combines a self-adaptive dose, frame rate adjustment and multi-modal motion compensation method, and the dynamic DR imaging module is used for performing dynamic DR lung blood flow perfusion diagnosis by adopting a first acquisition mode evaluation and dynamic parameter calibration strategy. According to the method, the radiation dose can be strictly controlled, meanwhile, the collected dynamic DR image can be processed through infrared respiratory gating, personalized lung field positioning and a non-local mean filtering algorithm combined with attention guidance, it is ensured that the processed dynamic DR image data is free of artifacts, full in coverage and high in fidelity, and the image quality of the dynamic DR is greatly improved. Meanwhile, the problems that the prior art mainly develops in the two directions of static dissection imaging and dynamic function imaging, but has significant shortages and is difficult to meet the clinical requirements of low radiation, high dynamic and full view are solved.