Examples described herein relate to automatic identification and transformation of a color region. A user can identify a region of a video frame or image that corresponds to a color region that is to be segmented. A color region can include one or more colors that appear to be approximately a uniform color. For one or more video frames, gamma correction can be applied to frames of the video. One or more frames of a video can be mapped to two color spaces. For each pixel in an image, a determination is made if the pixel has the same color as that of the identified region based on each of the at least two color spaces identifying the pixel as the color. The color region can be identified throughout a video and transformed to another color to aid in video editing.

A system and method of 3-D image segmentation of brain images includes obtaining a 3-D MRI image, an employee phase including performing search cycles of generating solutions in a neighborhood, taking into account (a) movement of a bee's current location toward a mean value of a positive direction of a global best location and a positive direction of its own best location, (b) movement of the bee's current location toward the mean value of the positive direction of its own best location and a negative direction of the global best location, and (c) a random number, calculating a fitness value for the solutions based on membership values of pixels and distances between the pixels to cluster centers of pixels until search ends. Image segmentation of the image is performed using centers of clusters.

Various embodiments of the present disclosure are directed towards a method for generating a risk group classification for an African American (AA) patient. The method includes extracting a first plurality of architectural features from a digitized H&E slide image of the AA patient. A risk score for the AA patient is generated based on the first plurality of architectural features, where the risk score is prognostic of overall survival (OS) of the AA patient. The risk group classification is generated for the AA patient, where generating the risk group classification includes classifying the AA patient into either a high risk group or a low risk group based on the risk score, where the high risk group indicates the AA patient will die before a threshold date and the low risk group indicates the AA patient will die after or on the threshold date.

A method for analyzing a rock sample includes segmenting a digital image volume corresponding to an image of the rock sample, to associate voxels in the digital image volume with a plurality of rock fabrics of the rock sample. The method also includes identifying a set of digital planes through the digital image volume. The set of digital planes intersects with each of the plurality of rock fabrics. The method further includes machining the rock sample to expose physical faces that correspond to the identified digital planes, performing scanning electron microscope (SEM) imaging of the physical faces to generate two-dimensional (2D) SEM images of the physical faces, and performing image processing on the SEM images to determine a material property associated with each of the rock fabrics.

A method for analyzing a rock sample includes segmenting a digital image volume corresponding to an image of the rock sample, to associate voxels in the digital image volume with a plurality of rock fabrics of the rock sample. The method also includes identifying a set of digital planes through the digital image volume. The set of digital planes intersects with each of the plurality of rock fabrics. The method further includes machining the rock sample to expose physical faces that correspond to the identified digital planes, performing scanning electron microscope (SEM) imaging of the physical faces to generate two-dimensional (2D) SEM images of the physical faces, and performing image processing on the SEM images to determine a material property associated with each of the rock fabrics.

Illustrative embodiments are directed to methods, apparatus and computer program products for segmentation of X-ray tomography images via multiple reconstructions. A computed tomography scan of an object is received. The computed tomography scan is processed to generate an absorption reconstruction and a phase reconstruction from the computed tomography scan. First and second sets of seeds within the phase reconstruction are labeled as corresponding to a first phase by thresholding below a first threshold and corresponding to a second phase by thresholding above a second threshold, respectively. The absorption reconstruction is segmented automatically using an algorithm based on the first set of seeds, the second set of seeds and the absorption reconstruction. A final segmentation is produced based on a combination of the absorption reconstruction and the phase reconstruction.

Illustrative embodiments are directed to methods, apparatus and computer program products for segmentation of X-ray tomography images via multiple reconstructions. A computed tomography scan of an object is received. The computed tomography scan is processed to generate an absorption reconstruction and a phase reconstruction from the computed tomography scan. First and second sets of seeds within the phase reconstruction are labeled as corresponding to a first phase by thresholding below a first threshold and corresponding to a second phase by thresholding above a second threshold, respectively. The absorption reconstruction is segmented automatically using an algorithm based on the first set of seeds, the second set of seeds and the absorption reconstruction. A final segmentation is produced based on a combination of the absorption reconstruction and the phase reconstruction.

An image processing method, an image processing device, an electronic device, and a storage medium are provided. The method comprises: performing image segmentation on a first image which is to be processed, obtaining an initial mask image according to an image segmentation result, in response to determining that the first image satisfies a preset sky area replacement condition according to the initial mask image, obtaining a target mask image by performing guided filtering on the initial mask image by using a greyscale image of the first image as a guide image, acquiring a target sky scene, and obtaining a second image by performing replacement on the sky area in first the image according to the target mask image and the target sky scene.

An image processing method, an image processing device, an electronic device, and a storage medium are provided. The method comprises: performing image segmentation on a first image which is to be processed, obtaining an initial mask image according to an image segmentation result, in response to determining that the first image satisfies a preset sky area replacement condition according to the initial mask image, obtaining a target mask image by performing guided filtering on the initial mask image by using a greyscale image of the first image as a guide image, acquiring a target sky scene, and obtaining a second image by performing replacement on the sky area in first the image according to the target mask image and the target sky scene.

Embodiments of a system, a machine-accessible storage medium, and a computer-implemented method are described in which operations are performed. The operations comprising receiving a plurality of image frames associated with a video of an endoscopy procedure, generating a probability estimate for one or more image frames included in the plurality of image frames, and identifying a transition in the video when the endoscopy procedure transitions from a first phase to a second phase based, at least in part, on the probability estimate for the one or more image frames. The probability estimate includes a first probability that one or more image frames are associated with a first phase of the endoscopy procedure.