According to an embodiment of the disclosure, an electronic device may include: a display, a memory, and a processor operatively connected to the display and the memory. According to an embodiment, the memory may store instructions that, when executed, cause the processor to: obtain a first image of a first shape, obtain linear information indicating a morphological characteristic of an object in the first image of the first shape, determine a conversion method for converting the first image of the first shape into an image of a second shape based on the obtained linear information, convert the first image of the first shape into a second image of the second shape based on the determined conversion method, and control the display to display the converted second image of the second shape on the display.

Provided herein are systems and methods for automated identification of volumes of interest in volumetric brain images using artificial intelligence (AI) enhanced imaging to diagnose and treat acute stroke. The methods can include receiving image data of a brain having header data and voxel values that represent an interruption in blood supply of the brain when imaged, extracting the header data from the image data, populating an array of cells with the voxel values, applying a segmenting analysis to the array to generate a segmented array, applying a morphological neighborhood analysis to the segmented array to generate a features relationship array, where the features relationship array includes features of interest in the brain indicative of stroke, identifying three-dimensional (3D) connected volumes of interest in the features relationship array, and generating output, for display at a user device, indicating the identified 3D volumes of interest.

The present invention discloses a method and system for recognizing a geometric regularity image of a honeycomb structure. The method includes the steps of image acquisition, image processing, vertex extraction, cell reconstruction, and quality evaluation, wherein a step of binaryzation is set between the step of image processing and the step of vertex extraction, and is to set a pixel value of a background in the image to be 0 and set a pixel value of a honeycomb skeleton in the image to be 1 to form a binary image, and the step of quality evaluation is to calculate angular deviation values of all the cells and an average thereof as well as linear deviation values and an average thereof based on the reconstructed cell image, and determine whether the honeycomb structure is qualified or not by comparing with a set tolerance zone.

Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are commonly used to assess patients with known or suspected pathologies of the lungs and liver. In particular, identification and quantification of possibly malignant regions identified in these high-resolution images is essential for accurate and timely diagnosis. However, careful quantitative assessment of lung and liver lesions is tedious and time consuming. This disclosure describes an automated end-to-end pipeline for accurate lesion detection and segmentation.

Systems, methods, software and techniques are disclosed for morphing a generic virtual boundary into a patient-specific virtual boundary for an anatomical model. The generic virtual boundary comprises one or more morphable faces. An intersection of the generic virtual boundary and the anatomical model is computed to define a cross-sectional contour of the anatomical model. One or more faces of the generic virtual boundary are morphed to conform to the cross-sectional contour of the anatomical model to produce the patient-specific virtual boundary. In some cases, the morphed faces are spaced apart from the cross-sectional contour by an offset distance that accounts for a geometric feature of a surgical tool.

A method and apparatus are disclosed which enable the analysis of a break in a vehicle glazing panel without the attendance of a technician, the method and apparatus utilize capturing an image of the break and processing the image of the break to enable the suitability for repair or replacement of the glazing panel to be determined.

The present invention belongs to the technical field of petroleum exploitation engineering, and discloses a 3D modeling method for cementing hydrate sediment based on a CT image. Indoor remolding rock cores or in situ site rock cores without hydrate can be scanned by CT; a sediment matrix image stack and a pore image stack are obtained by gray threshold segmentation; then, a series of cementing hydrate image stacks with different saturations are constructed through image morphological processing of the sediment matrix image stack such as dilation, erosion and image subtraction operation; and a series of digital rock core image stacks of the cementing hydrate sediment with different saturations are formed through image subtraction operation and splicing operation to provide a relatively real 3D model for the numerical simulation work of the basic physical properties of a reservoir of natural gas hydrate.

A pupil tracking method includes: retrieving an external eye image of a subject, wherein the external eye image includes a pupil of the subject; performing an image preprocessing on the external eye image, wherein the image preprocessing includes performing a binary conversion on the external eye image to obtain a binary image; finding out a contour boundary of each feature in the binary image, and finding out a pupil feature based on a variance of a distance from the contour boundary of each feature to a corresponding reference point; fitting the contour boundary of the pupil feature by a boundary fitting method to find a center coordinate of the pupil feature. The abovementioned pupil tracking method can track the pupil of the subject's eyeball without using a stereo camera. An ophthalmology inspection device using the abovementioned pupil tracking method is also disclosed.

Disclosed is a quaternion multi-degree-of-freedom neuron-based multispectral welding image recognition method, comprising: using three cameras having different wavebands to obtain multispectral weld pool images, and respectively performing pre-processing and edge extraction on the weld pool images having the different wavebands obtained at a same moment by the three cameras; establishing a quaternion-based multispectral weld pool image edge model; extracting low-frequency features after a quaternion discrete cosine transform; using a quaternion-based multi-degree-of-freedom neuron network to perform classification, training and recognition on edge features of the multispectral weld pool images. Compared to traditional means, the present invention has multiple recognition information sources, strong anti-interference capabilities and high recognition accuracy.

A computer implemented method can decode a frame of video data comprising an array of pixels to obtain decoded luma values and decoded chroma values corresponding to the array of pixels, and extract a frame mask based on the decoded luma values. The frame mask can include an array of mask values respectively corresponding to the array of pixels. A mask value indicates whether a corresponding pixel is in foreground or background of the frame. The method can perform a morphological operation to the frame mask to change one or more mask values to indicate their corresponding pixels are removed from the foreground and added to the background of the frame. The method can also identify foreground pixels after performing the morphological operation to the frame mask, and render a foreground image for display based on the decoded luma values and decoded chroma values of the foreground pixels.