5 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 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.

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.

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.