A novel methodology for computing the medial axis/skeleton of a discrete binary object using a divide and conquer algorithm, in which any 3D object is first sliced into a series of 2D images in X, Y and Z directions. Then, a geometric (Voronoi) algorithm is applied on each 2D image to extract the respective medial axis. This information is then combined to reconstruct the medial axis of the original 3D object using an intersection technique. An optional 3D interpolation step to achieve continuous connected skeletons uses Delaunay triangles and a spherical search to establish the nearest neighboring points in 3D space to interpolate between. Test results show that the proposed 3D Voronoi and optional interpolation algorithms are able to accurately and efficiently extract medial axes for complex 3D objects as well. Finally, an axis-smoothing algorithm using the same Delaunay triangle and spherical test is operable to remove unwanted noise from the extracted medial axis.

Systems and methods of improving segmentation and classification in multi-channel images of biological specimens are described herein. Image channels may be arranged and processed sequentially, with the first image channel being processed from an unmodified image, and subsequent images processed from attenuated images that remove features of previous segmentations. For each segmented image channel in sequence, a binary image mask of the image channel may be created. A distance transform image may be computed from the binary mask. Attenuation images computed for all previous channels may be combined with a current attenuation image to create a combined attenuation image. The next image channel in the sequence may then be attenuated to produce an attenuated next image channel, which may then be segmented. The steps can be repeated until all image channels have been segmented.

Mechanisms for generating a true depth profile of a body of water are disclosed. A depth profile tensor that identifies a depth at each of a plurality of locations of the body of water is accessed. The depth profile tensor identifies, for at least some locations of the plurality of locations, multiple depths. The depth profile tensor is converted to a binary potential depth image that depicts multiple potential depths for the at least some locations. The multiple potential depths are reduced, by a morphological filter process, to a single depth for the at least some locations to generate a binary depth image. The binary depth image is converted to the true depth profile.

A data processing unit receives a reference image (IMG1.sub.3D) of a deformable physical entity, a target image (IMG2.sub.3D) of said physical entity, and a first region of interest (ROI1.sub.3D) defining a first volume in the reference image (IMG1.sub.3D) representing a reference image element. The reference image (IMG1.sub.3D), the target image (IMG2.sub.3D) and the first region of interest (ROI1.sub.3D) all contain 3D datasets. In response to user commands (c1; c2), the data processing unit defines a first contour (C1.sub.2D) in a first plane through the target image (IMG2.sub.3D), which is presented to a user via a display unit together with graphic data reflecting the reference image (IMG1.sub.3D), the target image (IMG2.sub.3D) and the first region of interest (ROI1.sub.3D). The first contour (C1.sub.2D) is aligned with at least a portion of a first border (IEB1) of a target image element (IE.sub.3D) in the target image (IMG2.sub.3D). The target image element (IE.sub.3D) corresponds to the reference image element in the reference image (IMG1.sub.3D). Based on the first contour (C1.sub.2D), the target image (IMG2.sub.3D) and the first region of interest (ROI1.sub.3D); the data processing unit determines a second region of interest (ROI2.sub.3D) defining a second volume in the target image (IMG2.sub.3D).

A computer implemented method for determining a centerline of a three-dimensional tubular structure is described. The method includes providing an edge-detected data set of voxels that characterize a boundary of the tubular structure according to a three-dimensional voxel data set for the tubular structure. A gradient field of a distance transformation is computed for the edge-detected dataset. A voxel data set corresponding to a centerline of the tubular structure is computed according to derivative of gradient field.

An image of a physical environment is acquired that comprises a plurality of pixels, each pixel including a two-dimensional pixel location in the image plane and a depth value corresponding to a distance between a region of the physical environment and the image plane. For each pixel, the two dimensional pixel location and the depth value is converted into a corresponding three-dimensional point in the physical environment defined by three coordinate components, each of which has a value in physical units of measurement. A set of edge points is determined within the plurality of three-dimensional points based, at least in part, on the z coordinate component of the plurality of points and a distance map is generated comprising a matrix of cells. For each cell of the distance map, a distance value is assigned representing a distance between the cell and the closest edge point to that cell.

Apparatus, methods, and computer-readable media are provided for segmentation, processing (e.g., preprocessing and/or postprocessing), and/or feature extraction from tissue images such as, for example, images of nuclei and/or cytoplasm. Tissue images processed by various embodiments described herein may be generated by Hematoxylin and Eosin (H&E) staining, immunofluorescence (IF) detection, immunohistochemistry (IHC), similar and/or related staining processes, and/or other processes. Predictive features described herein may be provided for use in, for example, one or more predictive models for treating, diagnosing, and/or predicting the occurrence (e.g., recurrence) of one or more medical conditions such as, for example, cancer or other types of disease.

Methods and apparatuses in which a plurality of images are recorded at different illumination angles are provided. The plurality of images are combined in order to produce a results image with an increased depth of field.

Provided is a method for bone suppression in an X-ray image, which includes: extracting an upper contour line and a lower contour line corresponding to a bone to be suppressed from the original X-ray image; generating a first binarization image and a second binarization image based on the upper contour line and the lower contour line, respectively; generating a first distance transform image and a second distance transform image from the first binarization image and the second binarization image, respectively through distance transform; generating a compensated first X-ray image and a compensated second X-ray image by compensating a pixel value of a region which belongs to the bone by using the first distance transform image and the second distance transform image, respectively from the original X-ray image; and synthesizing the compensated first X-ray image and the compensated second X-ray image to obtain a bone-suppressed X-ray image.

A computer implemented method for determining a centerline of a three-dimensional tubular structure is described. The method includes providing an edge-detected data set of voxels that characterize a boundary of the tubular structure according to a three-dimensional voxel data set for the tubular structure. A gradient field of a distance transformation is computed for the edge-detected dataset. A voxel data set corresponding to a centerline of the tubular structure is computed according to derivative of gradient field.