Provided are methods for determining data defining an estimate of the thickness and density of a cortical bone tissue structure of interest from imaging data. The methods can include modelling measured variations of an imaging parameter along a line crossing a cortical bone tissue structure of interest as a function having a thickness parameter, a first density parameter, and a blur parameter; determining a thickness-density relationship between bone tissue structure density and bone tissue structure thickness from multiple thickness and density measurements made on a reference cortical bone tissue structure of a subject which is not the patient; and fitting the function to the measured variations while ensuring the first density parameter and thickness parameter follow the thickness-density relationship, to search for optimal values that include data defining an estimate of the thickness and density of the cortical bone tissue structure of interest. Also provided are systems and computer programs to implement the disclosed methods.

A method for assessing blood vessel stenosis using image data of a subject is disclosed. The image data represents a vascular structure of the subject. The method comprises: (a) segmenting, from the image data, a vessel segment representing a segment of a blood vessel, (b) obtaining, using the image data, a plurality of two-dimensional images of the vessel segment; said plurality of two-dimensional images representing respective cross-sections of the vessel segment, (c) identifying, for each of the plurality of two-dimensional images, a lumen area comprising lumen pixels representing a lumen of the corresponding cross-section, (d) obtaining a quantitative measure using the lumen areas of successive cross-sections of the vessel segment, and (e) assessing blood vessel stenosis using the quantitative measure. A computer system for performing the above method is disclosed.

An inspection method includes an image-data acquisition step of acquiring data about an image of a disk-shaped graduation plate as disk-shaped graduation-plate image data, and a polar-coordinate transformation step of transforming the disk-shaped graduation-plate image data into polar coordinates using a center of the disk-shaped graduation plate as a reference to generate polar-coordinate graduation image data. A defect detection step includes a processing-region setting step of setting a processing region for each of graduation line on a polar-coordinate angle display axis, a center-of-gravity calculation step of calculating a center of gravity for each processing region, and a center-of-gravity pitch calculation step of calculating a pitch of the center of gravity calculated in the center-of-gravity calculation step. the defect detection is executed by comparing a pitch of graduations in the polar-coordinate graduation image data with a predetermined reference value

A medical imaging system (5) includes a workstation (20), a coarse segmenter (30), a fine segmenter (32), and an enclosed tissue identification module (34). The workstation (20) includes at least one input device (22) for receiving a selected location as a seed in a first contrasted tissue type and a display device (26) which displays a diagnostic image delineating a first segmented region of a first tissue type and a second segmented region of a second contrasted tissue type and identified regions which include regions fully enclosed by the first segmented region as a third tissue type. The coarse segmenter (30) grows a coarse segmented region of coarse voxels for each contrasted tissue type from the seed location based on a first growing algorithm and a growing fraction for each contrasted tissue type. The seed location for growing the second contrasted tissue type includes the first coarse segmented region and any fully enclosed coarse voxels, and each coarse voxel includes an aggregation of voxels and a maximum and a minimum of the voxel intensities. The fine segmenter (32) grows a segmented region of voxels for each contrasted tissue type from the seed location and bounded by the second coarse segmented region based on a second growing algorithm and a growing fraction for each contrasted tissue type initially set to the growing fraction for the corresponding region. The seed location for growing the second contrasted tissue type includes the first segmented region and any identified regions. The enclosed tissue identification module (34) identifies any regions of voxels fully enclosed by the first segmented region as being of the third tissue type. The coarse segmenter, the fine segmenter, and the enclosed tissue identification module are implemented by an electronic data processing device.

A first detection unit detects a breast region and a skin line from a breast image, and a first index value calculation unit calculates a first index value indicating the single composition degree of the breast region. A second detection unit detects a boundary between the adipose tissue and the mammary gland tissue in a predetermined range from the skin line toward the inside of the breast region in the breast image. A second index value acquisition unit acquires a second index value indicating the degree of clogging of mammary glands with respect to the breast region based on at least one of the strength of the boundary or the distance from the skin line. An identification unit identifies the type of the breast based on the first and second index values.

A device for segmenting an image of a subject (36), includes a data interface for receiving an image of the subject (36), which image depicts a structure of said subject (36). A translation unit translates a user-initiated motion of an image positioner into a first contour (38) surrounding said structure. A motion parameter registering unit registers a motion parameter of said user-initiated motion to said first contour (38). The motion parameter includes a speed and/or an acceleration of an image positioner. An image control point unit distributes a plurality of image control points (40) on the first contour with a density decreasing with the motion parameter. A segmentation unit segments the image by determining a second contour (44) within the first contour based on the plurality of image control points (40). The segmentation unit is configured to use one or more segmentation functions.

A breast imaging apparatus includes a gantry including a radiation generating unit and a radiation detecting unit configured to detect the radiation emitted from the radiation generating unit, wherein the radiation generating unit and the radiation detecting unit can be rotated in an opposed state, an extraction unit configured to extract first tissues and a second tissue from a three-dimensional image based on a projection image output from the radiation detecting unit, a classification unit configured to classify a group of the first tissues with reference to the second tissue, and a display configured to distinguishably display the group of the first tissues classified by the classification unit.

A method, for two-dimensional mapping of anatomical structures of a patient, includes acquiring three-dimensional image data of anatomical structures of a patient; adapting a virtual network structure to a spatial course of the anatomical structures; defining a user-defined map projection for projection of two-dimensional pixel positions of an image to be output onto a geometric figure around a center of the anatomical structures for which mapping onto a two-dimensional space is defined; ascertaining points of intersection of radially extending half lines assigned to the two-dimensional pixel positions of the image to be output with the virtual network structure; and ascertaining the image to be output based upon image intensity values assigned to the points of intersection ascertained. A method for two-dimensional mapping of the tree-like elongated structure of the patient; a method for simultaneous mapping of a tree-like elongated structure; and corresponding apparatuses are also described.

Embodiments of the present invention provide techniques for coding video data efficiently based on detection of objects within video sequences. A video coder may perform object detection on the frame and when an object is detected, develop statistics of an area of the frame in which the object is located. The video coder may compare pixels adjacent to the object location to the object's statistics and may define an object region to include pixel blocks corresponding to the object's location and pixel blocks corresponding to adjacent pixels having similar statistics as the detected object. The coder may code the video frame according to a block-based compression algorithm wherein pixel blocks of the object region are coded according to coding parameters generating relatively high quality coding and pixel blocks outside the object region are coded according to coding parameters generating relatively lower quality coding.

A method for assessing blood vessel stenosis using image data of a subject is disclosed. The image data represents a vascular structure of the subject. The method comprises: (a) segmenting, from the image data, a vessel segment representing a segment of a blood vessel, (b) obtaining, using the image data, a plurality of two-dimensional images of the vessel segment; said plurality of two-dimensional images representing respective cross-sections of the vessel segment, (c) identifying, for each of the plurality of two-dimensional images, a lumen area comprising lumen pixels representing a lumen of the corresponding cross-section, (d) obtaining a quantitative measure using the lumen areas of successive cross-sections of the vessel segment, and (e) assessing blood vessel stenosis using the quantitative measure. A computer system for performing the above method is disclosed.