Methods for improving and modifying a High Dynamic Range (HDR) scene, captured as a series of images of the scene with different exposure levels and the scene through classification-based image merging, tuning, correction, and replacement. The approach employs mixing images to improve the selection and display of both shadowed and highlighted details. The increased efficiency resulting from improvements in computational resource utilization of image processing hardware can, from the implementation of the improved computational methods herein, significantly reduce the time required to generate and display a tone-mapped HDR image, a gamma-corrected HDR image, and/or a segmented and replaced HDR image.

Methods for improving and modifying a High Dynamic Range (HDR) scene, captured as a series of images of the scene with different exposure levels and the scene through classification-based image merging, tuning, correction, and replacement. The approach employs mixing images to improve the selection and display of both shadowed and highlighted details. The increased efficiency resulting from improvements in computational resource utilization of image processing hardware can, from the implementation of the improved computational methods herein, significantly reduce the time required to generate and display a tone-mapped HDR image, a gamma-corrected HDR image, and/or a segmented and replaced HDR image.

A method may include obtaining an image representing a region of interest (ROI) of an object. The ROI may include two or more sub-regions. The method may include determining an average value of quantitative indexes associated with elements in the image corresponding to a first region of the ROI. The method may include determining, for each of the two or more sub-regions of the ROI, a threshold based on the average value; identifying target elements in the image based on the thresholds of the two or more sub-regions. The method may include assigning a presentation value to each of at least some of the target elements based on the average value and the quantitative index of the each target element. The method may include generating a presentation of the image based on the presentation values.

The present disclosure relates to an imaging system and method. Specifically, an imaging system comprises: a positioning image acquisition unit, configured to acquire a positioning image of a scanning object; a monitoring slice image acquisition unit, configured to determine a key point corresponding to the position of a target region of interest in the positioning image by using a neural network, and acquire a monitoring slice image of the scanning object at the position of the key point; and a target region-of-interest segmentation unit, configured to segment the monitoring slice image to obtain the target region of interest. The present disclosure can accurately acquire the position of the monitoring slice, and can accurately obtain the target region of interest through segmentation by a cascaded coarse segmentation and fine segmentation.

The present disclosure relates to an imaging system and method. Specifically, an imaging system comprises: a positioning image acquisition unit, configured to acquire a positioning image of a scanning object; a monitoring slice image acquisition unit, configured to determine a key point corresponding to the position of a target region of interest in the positioning image by using a neural network, and acquire a monitoring slice image of the scanning object at the position of the key point; and a target region-of-interest segmentation unit, configured to segment the monitoring slice image to obtain the target region of interest. The present disclosure can accurately acquire the position of the monitoring slice, and can accurately obtain the target region of interest through segmentation by a cascaded coarse segmentation and fine segmentation.

The method according to the invention utilizes a color decomposition of histological tissue image data to derive a density map. The density map corresponds to the portion of the image data that contains the stain/tissue combination corresponding to the stroma, and at least one gland is extracted from said density map. The glands are obtained by a combination of a mask and a seed for each gland derived by adaptive morphological operations, and the seed is grown to the boundaries of the mask. The method may also derive an epithelial density map used to remove small objects not corresponding to epithelial tissue. The epithelial density map may further be utilized to improve the identification of glandular regions in the stromal density map. The segmented gland is extracted from the tissue data utilizing the grown seed as a mask. The gland is then classified according to its associated features.

The method according to the invention utilizes a color decomposition of histological tissue image data to derive a density map. The density map corresponds to the portion of the image data that contains the stain/tissue combination corresponding to the stroma, and at least one gland is extracted from said density map. The glands are obtained by a combination of a mask and a seed for each gland derived by adaptive morphological operations, and the seed is grown to the boundaries of the mask. The method may also derive an epithelial density map used to remove small objects not corresponding to epithelial tissue. The epithelial density map may further be utilized to improve the identification of glandular regions in the stromal density map. The segmented gland is extracted from the tissue data utilizing the grown seed as a mask. The gland is then classified according to its associated features.

A human region detection unit 32 of a mask generation unit 31 detects a target region from a captured image using a region determination result obtained by semantic segmentation, and a difference region detection result. A mask generation processing unit 33 resets a boundary between a target region and a non-target region on the basis of continuity of a pixel value of a captured image, in a boundary re-search region set to include a target region and a non-target region on the basis of a boundary between the target region and the non-target region, such as a background region, that is indicated by a region determination result, and generates a target region mask using the reset boundary. A filtering unit 35 generates an image in which a background region is blurred, by performing filter processing of a region in a captured image that corresponds to a target region mask, using a target region mask generated by the mask generation unit 31, and a blurring filter coefficient set by a filter setting unit 34. It becomes possible to perform background blurring with a small amount of artifact.

The present embodiments relate generally to systems and methods for performing a measurement on an ultrasound image displayed on a touchscreen device. The method may include: receiving, via the touchscreen device, first input coordinates corresponding to a point on the ultrasound image; using the first input coordinates as a seed for performing a contour identification process on the ultrasound image, wherein the contour identification process performs contour evolution using morphological operators to iteratively dilate from the first input coordinates; upon identification of a contour from the contour identification process, placing measurement calipers on the identified contour; and storing a value identified by the measurement calipers as the measurement.

The present embodiments relate generally to systems and methods for performing a measurement on an ultrasound image displayed on a touchscreen device. The method may include: receiving, via the touchscreen device, first input coordinates corresponding to a point on the ultrasound image; using the first input coordinates as a seed for performing a contour identification process on the ultrasound image, wherein the contour identification process performs contour evolution using morphological operators to iteratively dilate from the first input coordinates; upon identification of a contour from the contour identification process, placing measurement calipers on the identified contour; and storing a value identified by the measurement calipers as the measurement.