A system and method for image segmentation are provided. A three-dimensional image data set representative of a region including at least one airway may be acquired. The data set may include a plurality of voxels. A first-level seed within the region may be identified. A first-level airway within the region may be identified based on the first-level seed. A second-level airway may be identified within the region based on the first-level airway. The first-level airway and the second-level airway may be fused to form an airway tree.

To increase accuracy in extracting a foreground area while saving a user time and effort. Image obtaining means of an image processing device obtains an image including a background and an object. Element area setting means sets, with respect to the image, a plurality of element areas respectively corresponding to a plurality of elements in the image. Overlapped area specifying means specifies an overlapped area in which a degree of overlap of the element areas is greater than or equal to a predetermined value in the image. Foreground area extracting means extracts a foreground area corresponding to the object from the image based on the overlapped area.

A method and system for patient-specific cardiac electrophysiology is disclosed. Particularly, a patient-specific anatomical model of a heart is generated from medical image data of a patient, a level-set representation of the patient-specific anatomical model is generated of the heart on a Cartesian grid; and a transmembrane action potential at each node of the level-set representation of the of the patient-specific anatomical model of the heart is computed on a Cartesian grid.

The application discloses a method and system for segmenting a lung image. The method may include obtaining a target image relating to a lung region. The target image may include a plurality of image slices. The method may also include segmenting the lung region from the target image, identifying an airway structure relating to the lung region, and identifying one or more fissures in the lung region. The method may further include determining one or more pulmonary lobes in the lung region.

A system and method for image segmentation are provided. A three-dimensional image data set representative of a region including at least one airway may be acquired. The data set may include a plurality of voxels. A first-level seed within the region may be identified. A first-level airway within the region may be identified based on the first-level seed. A second-level airway may be identified within the region based on the first-level airway. The first-level airway and the second-level airway may be fused to form an airway tree.

A method and system of automated segmentation of an anatomical object through learned examples include: receiving, by a processing device, an image of the anatomical object; determining a sparse representation of a shape of the anatomical object by iteratively evolving a segmenting surface as a combination of a level set segmentation and a linear combination of training shapes; and outputting, to an output device, the sparse representation of the shape of the anatomical object as the segmentation of the anatomical object.

The present invention relates to an ultrasound imaging system (10) for inspecting an object (97) in a volume (40). The ultrasound imaging system comprises an image processor (36) configured to conduct a segmentation (80) of the object (97) simultaneously out of three-dimensional ultrasound mage data (62) and contrast-enhanced three-dimensional ultrasound image data (60). In particular, this may be done by minimizing an energy term taking into account both the normal three-dimensional ultrasound image data and the contrast-enhanced three-dimensional image data. By this, the normal three-dimensional ultrasound image data and the contrast-enhanced three-dimensional image data may even be registered during segmentation. Hence, this invention allows a more precise quantification of one organ in two different modalities as well as the registration of two images for simultaneous visualization.

Systems and methods for managing motions of an anatomical region of interest of a patient during image-guided radiotherapy are disclosed. An exemplary system may include an image acquisition device, a radiotherapy device, and a processor device. The processor device may be configured to control the image acquisition device to acquire at least one 2D image. Each 2D image may include a cross-sectional image of the anatomical region of interest. The processor device may also be configured to perform automatic contouring in each 2D image to extract a set of contour elements segmenting the cross-sectional image of the anatomical region of interest in that 2D image. The processor device may be further configured to match the set of contour elements to a 3D surface image of the anatomical region of interest to determine a motion of the anatomical region of interest and to control radiation delivery based on the determined motion.

A computer implemented image processing method is disclosed. The method comprises: obtaining microscope image data defining a microscope slide image of a haematoxylin and eosin stained tissue sample, wherein the microscope slide image data comprises a plurality of image pixels; obtaining descriptor data indicating a type of tissue from which the tissue sample originates; selecting, based on the descriptor data, an image operation configured to transform the image data; applying the selected image operation to the image data to identify a number of discrete spatial regions of the image; selecting, from a data store, a set of quantitative image metrics wherein the quantitative image metrics are selected based on the descriptor data, determining, for each discrete spatial region, a sample region data value for each of the set of quantitative image metrics based on the subset of image data associated with the or each discrete spatial region, using the descriptor data to select, from the data store, at least one comparator set of tissue model data values, wherein each comparator set is associated with a different corresponding comparator tissue structure and each comparator set comprises data values of the set of quantitative image metrics for the corresponding comparator tissue structure; comparing the sample region data value for each discrete region with the at least one comparator set; and in the event that the sample region data value for the or each discrete spatial region matches the comparator set providing a map of the image data indicating that the discrete spatial region comprises the matching comparator tissue structure.

A method for assessing a three-dimensional (3D) surface area having one or more lesions is disclosed. The method includes steps of: capturing a two-dimensional (2D) color image and a depth image of the 3D surface area; enhancing contrast of the 2D color image; segmenting the one or more lesions of the 2D color image into one or more segmented lesions; and calculating 3D area of the one or more segmented lesions using information from 2D color image and the depth image.