A distance measurement method is provided. The distance measurement method includes obtaining an image of a target device, the image of the target device including a target object of a first color and a background object of a second color, the first color being different from the second color; processing the image of the target device to obtain a processed image, the processed image including a processed target object of a third color and a processed background object of a fourth color; detecting a contour of the processed target object of the third color; calculating an area encircled by the contour; and calculating a distance between a camera and the target device upon determination of the area encircled by the contour.

Disclosed herein are systems and methods for vertebral bone segmentation and vertebral bone enhancement in medical images.

This application disclosures a method and system for detecting a centerline of a vessel. The method may include obtaining image data, wherein the image data may include vessel data; selecting two endpoints of the vessel based on the vessel data; transforming the image data to generate a transformed image based on at least one image transformation function; and determining a path of the centerline of the vessel connecting the first endpoint of the vessel and the second endpoint of the vessel to obtain the centerline of the vessel based on the transformed image. The two endpoints of the vessel may include a first endpoint of the vessel and a second endpoint of the vessel.

Methods and systems for image processing are provided. Image data may be obtained. The image data may include a plurality of voxels corresponding to a first plurality of ribs of an object. A first plurality of seed points may be identified for the first plurality of ribs. The first plurality of identified seed points may be labelled to obtain labelled seed points. A connected domain of a target rib of the first plurality of ribs may be determined based on at least one rib segmentation algorithm. A labelled target rib may be obtained by labelling, based on a hit-or-miss operation, the connected domain of the target rib, wherein the hit-or-miss operation may be performed using the labelled seed points to hit the connected domain of the target rib.

The purpose is to provide an ultrasound imaging device capable of automatically detecting a boundary of a biological tissue in an ultrasound image. An ultrasound imaging device includes an image generation module which receives ultrasound waves transmitted from a surface of an analyte toward an inside of the analyte and reflected therein to generate an ultrasound image inside the analyte, a reference point setting module which sets a reference point of a tissue of interest of the ultrasound image, a first seed point imparting module which imparts one or more seed points to the ultrasound image with reference point, and a region demarcating module which demarcates a region to which the seed point belongs and divides an image region of the analyte included in the ultrasound image into a plurality of regions according to a type of tissue.

A medical image processing apparatus according to an embodiment includes processing circuitry configured: to generate a projection image by implementing an intensity projection on a plurality of two-dimensional images structuring three-dimensional volume data rendering a tubular organ; to obtain a mapping matrix of the intensity projection; to annotate the tubular organ in the projection image; and to identify the tubular organ in the three-dimensional volume data, by inversely mapping the tubular organ annotated in the projection image onto the three-dimensional volume data while using the mapping matrix.

A mechanism is provided in a data processing system for refining lesion contours with combined active contour and inpainting. The mechanism receives an initial segmented medical image having organ tissue including a set of object contours and a contour to be refined. The mechanism inpaints object voxels inside all contours of the set. The mechanism calculates an updated contour around the contour to be refined based on the in-painted object voxels to form an updated segmented medical image. The mechanism determines whether the updated segmented medical image is improved compared to the initial segmented medical image. The mechanism keeps the updated segmented medical image responsive to the updated segmented medical image being improved.

Disclosed herein is a method and system for automatically propagating segmentation in a medical image. In an embodiment, the method uses a segmented reference Region of Interest (RoI) in a reference image to determine segmentation parameters and a plurality of reference points. Further, method generates a plurality of translated points on a current image, in which a target RoI must be segmented, by translating the plurality of reference points onto the current image. Subsequently, relevant seeds among from the translated points are automatically selected based on the segmentation parameters. Finally, a multi-seed segmentation of the selected relevant seeds is performed for estimating and segmenting the target RoI in the current image, such that the target RoI is the propagated segmentation of the segmented RoI in the reference image.

A method (and system) of segmenting one or more histological structures in a tissue image represented by multi-parameter cellular and sub-cellular imaging data includes receiving coarsest level image data for the tissue image, wherein the coarsest level image data corresponds to a coarsest level of a multiscale representation of first data corresponding to the multi-parameter cellular and sub-cellular imaging data. The method further includes breaking the coarsest level image data into a plurality of non-overlapping superpixels, assigning each superpixel a probability of belonging to the one or more histological structures using a number of pre-trained machine learning algorithms to create a probability map, extracting an estimate of a boundary for the: one or more histological structures by applying a contour algorithm to the probability map, and using the estimate of the boundary to generate a refined boundary for the one or more histological structures.

A system for generating an automatically segmented and annotated two-dimensional (2D) map of an environment includes processors coupled to a scanner to convert a 2D map from the scanner into a 2D image. Further, a mapping system categorizes a first set of pixels from the image into one of room-inside, room-outside, and noise by applying a trained neural network to the image. The mapping system further categorizes a first subset of pixels from the first set of pixels based on a room type if the first subset of pixels is categorized as room-inside. The mapping system also determines the room type of a second subset of pixels from the first set of pixels based on the first subset of pixels by using a flooding algorithm. The mapping system further annotates a portion of the 2D map to identify the room type based on the pixels corresponding to the portion.