13 results about "Window level" patented technology

The invention discloses a multi-modal data fusion method in electric power overhaul, which comprises the following steps of: firstly, respectively acquiring long-sequence video data and audio data in an electric power overhaul process, and constructing a window-level multi-modal data fusion model; secondly, performing time window division and coarse alignment on the long-sequence video data and audio data by the window-level multi-modal data fusion model, generating a frame-level quality score, and performing fine alignment on multi-modal characteristic data in a time window by adopting a segmented time sequence alignment and compensation algorithm; and calculating a window-level quality score, a window-level weak supervision label and a confidence coefficient, and finally performing multi-modal fusion according to the multi-modal feature data after fine alignment and the window-level weak supervision label to generate a window-level semantic representation as a model output. According to the method, accurate time alignment of video and audio data and consistent fusion of multi-modal semantic features are realized, and more reliable alignment data and semantic input are provided for subsequent target recognition.

This invention belongs to the field of medical image processing technology, specifically relating to an X-ray image enhancement method, system, and storage medium. The method includes: inputting an original X-ray image and reapplying window width and window level; performing noise reduction processing; copying the X-ray image into a normal tissue layer, a cancerous tissue layer, and a calcification point layer, adjusting the brightness and contrast of each layer using gamma adjustment; performing image enhancement and Retinex enhancement; acquiring the three enhanced layers and the Retinex-enhanced normal tissue layer, and performing Laplacian pyramid fusion to obtain the enhanced image. This invention also provides an X-ray image enhancement system for implementing the above method. This invention can achieve excellent image enhancement results without excessive magnification or contrast enhancement, supporting doctors in rapid and accurate diagnosis, and has excellent application prospects.

The X-ray CT image data should be displayed with an appropriate window width and window level when the energy value being displayed is changed. [Solution] The image processing apparatus according to the embodiment comprises a display control unit and a determination unit. The display control unit causes the display unit to display the first CT image data, which is displayed at a first keV value, at a first window width and a first window level. The determination unit determines the second window width and the second window level based on statistical information relating to the first window width, the first window level, the CT values ​​of a specific region corresponding to the first keV value and the second keV value, and the CT values ​​of a specific region corresponding to the first keV value and the second keV value. The display control unit causes the display unit to display the second CT image data at a second keV value at the determined second window width and second window level.

The embodiment of the invention discloses a radiation imaging method and a radiation imaging system. According to the pixel feature information of the target area in the radiation image and the window width and window level information of the radiation image, the tissue stable display coefficient of the target area is determined, and the tissue stable display coefficient is used for reflecting the black and white degree of the target area in the radiation image. Therefore, whether the display effect of the radiation image meets the requirements of a doctor can be determined through the tissue stable display coefficient. And if the tissue stability display coefficient is not within the preset numerical range, at least optimizing the display effect of the target area in the radiation image according to the pixel feature information of the target area, so that the tissue stability display coefficient of the optimized target area is within the preset numerical range. Therefore, the display effect of the radiation image can be adjusted according to the requirements of a doctor, so that the radiation image achieves the satisfactory display effect of the doctor, the doctor can conveniently observe the physiological structure characteristics represented by the radiation image, and the diagnosis work of the doctor is facilitated.

The application discloses a pancreas image segmentation method and system based on deep learning. The method comprises the following steps: acquiring an abdominal CT image sequence and performing window width and window level adjustment, resampling and region of interest cropping; loading pre-trained and fixed weight encoder weight and joint segmentation network model parameter, performing single forward propagation inference on the preprocessed image to obtain a segmentation probability map and a predicted shape vector; post-processing the segmentation probability map to output a three-dimensional pancreas segmentation mask; loading shape prior knowledge base, and combining the predicted shape vector to perform offline confidence evaluation on the segmentation result. The application effectively solves the segmentation fracture problem caused by the fuzzy pancreas boundary by fusing global shape prior constraints, and quantifies the reliability of the result through the confidence evaluation mechanism, thereby improving the precision and clinical applicability of pancreas segmentation.

The application discloses a kind of based on double branch contrast learning's heating system secondary network abnormality detection method and system, belong to industrial internet and heating system detection technical field, comprising: the multidimensional time series running data of heating system secondary network is collected to form time series sample set;Using double branch contrast learning model obtains the abnormal score of each sample in time series sample set, specifically: time series sample set is sequentially through sequence embedding module and window level feature fusion module, obtain enhanced time series feature representation and split into multiple feature blocks, using double attention module extracts the global feature and local feature of feature block;The abnormal score of each sample is calculated;Judge whether there is the abnormal score of sample exceeds preset threshold, if yes, consider that heating system secondary network exists anomaly and record abnormality occurrence time, otherwise, consider that heating system secondary network does not exist anomaly.No need to rely on pure training sample, can accurately detect abnormality and locate abnormality occurrence time.

The application provides a display control method, an electronic device and related apparatuses. The electronic device has at least two modes, which are a multitask scheduling mode and a non-multitask scheduling mode. In the non-multitask scheduling mode, the electronic device does not display a floating window of an application in response to an operation on an application icon. In the multitask scheduling mode, the electronic device displays the floating window of the application in a multitask scheduling interface in response to a user operation on the application icon. In the multitask mode, the electronic device displays an icon of a task group. In response to a click operation on the task group icon, the electronic device displays an application window according to one or more of a window size, a window position and a window level corresponding to the task group icon. The displayed content meets the user's expectation, thereby improving the user's experience.

The application discloses a pop-up window display and scheduling method for a car machine system. The method first acquires a pop-up window instance from a unified class library in response to an application call, which standardizes the visual implementation of the pop-up window and solves the problem of inconsistent styles caused by multi-source development. Secondly, the display level of the pop-up window instance is determined according to a predefined pop-up window level matrix, which sets clear coverage rules for application-level, system-level and high-level pop-up windows, thereby fundamentally avoiding level conflicts and occlusion between pop-up windows. Finally, through an independent scheduling center, the pop-up window is globally scheduled to be displayed or hidden based on the level and the real-time monitored interface state (such as application switching and pop-up window triggering events). The method decouples the creation, level management and behavior scheduling of the pop-up window at the system level and uniformly controls them, thereby ensuring the consistency of all pop-up windows of the car machine in vision and behavior, and improving the interactive experience and interface aesthetics.

This invention discloses an interactive hematoma segmentation and analysis method and system based on brain CT images. The method includes: acquiring brain medical images and setting window width and window level for the medical images; determining a first hematoma region based on the human-computer interaction selection area; performing three-dimensional median filtering; performing three-dimensional Otsu threshold segmentation; performing three-dimensional opening operation; selecting the largest three-dimensional connected component of the binary segmented image to obtain the segmentation result of the second hematoma region; obtaining the hematoma centroid and hematoma volume; and calculating the long axis direction of the hematoma using PCA. This method has a small memory footprint, is easy to integrate into software and deploy on various devices, and provides relatively complete hematoma information, effectively improving the calculation speed and hematoma segmentation accuracy. It can assist doctors or surgical robots in brain hematoma localization analysis and puncture path planning, and has the characteristics of lightweight, easy integration, ease of operation, information diversity, accuracy, and speed.

The application discloses a rail turnout area line automatic detection method, comprising the following steps: S1) using an overlapping sliding window to divide the original echo data collected by a rail flaw detection vehicle into window samples of a fixed length and to label; S2) for each window sample, mapping the echo data in the window into a time sequence feature matrix; S3) after inputting the time sequence feature matrix into a turnout area line detection model, outputting a time step level probability. In the training stage of the turnout area line detection model, the model parameters are trained by using a window level supervision loss and an optimization strategy. In the inference stage of the turnout area line detection model, the window level results are subjected to threshold determination, overlapping fusion and interval merging, and the turnout heart position range is output. The application can solve the technical problems of low detection efficiency, high false detection rate and difficult feature extraction of the existing rail flaw detection vehicle in the turnout area detection.

The invention provides an AI-based medical respiratory system diagnosis auxiliary method and system, and the method comprises the steps: obtaining a chest CT image from a PACS system, carrying out the preprocessing of lung segmentation, window level adjustment and the like, and generating a concept score vector through a concept activation encoder; the method specifically comprises the steps that a backbone network extracts a preprocessed image feature map, similarity is calculated according to the feature map and a predefined concept prototype library to generate an activation map, a high activation area of each concept prototype is determined, and average similarity is calculated to obtain a concept score; a diagnosis probability is generated by a microcosmic concept inference device, that is, output is generated by applying logic atoms (weight is set based on medical priori knowledge), and the diagnosis probability is generated through a classification layer in combination with a concept score vector; and finally, generating a structured report according to the diagnosis probability and the concept score vector. According to the method, diagnosis is decoupled into concept discovery, quantification and reasoning, interpretable decision making is achieved, the black box problem is solved, and a doctor can conveniently trace the AI judgment basis.

The application discloses a terminal window level control system based on local service, which comprises a webpage service module, a control service module and a terminal module. The terminal module loads and runs a service station webpage program of the webpage service module through an HTTP protocol, and initiates an API interface calling request to the control service module. The control service module generates a window level control instruction based on the request, and the terminal module executes the window level control instruction to realize window control service. The application decouples the window top logic from the main application program, adopts an independent HTTP service for management, and realizes a flexible, reliable and easy-maintained window level scheme.