Abstract
The mass of an object may be estimated based on intersection points of a representation of a surface in an image space with cubes defining the image space, the surface representing a surface of an object. The representation may be, for example, based on marching cubes. The mass may be estimated by estimating a mass contribution of a first set of cubes contained entirely within the representation of the surface, estimating a mass contribution of a second set of cubes having intersection points with the representation of the surface, and summing the estimated mass contribution of the first set of cubes and the estimated mass contribution of the second set of cubes. The object may be segmented from other portions of an image prior to estimating the mass of the object.
Claims

A system, comprising:
a memory; and
one or more processing devices configured to:
where u_{1 }is a vector component representing local movement within the surface, u_{2 }is a vector component representing local movement along surface normal, and u_{3 }is a vector component representing local movement deforming the surface toward a surface of the segmented object.determine intersection points of a representation of a surface in an image space with cubes defining the image space, the surface defining a surface of an object, the determining the intersection points including:
defining an initial representation of the surface in the image space;
refining the initial representation of the surface in the image space by segmenting the object and refining the initial representation to correspond to a segmented portion of the object; and
determining the intersection points of the refined representation of the surface with the cubes defining the image space; and
determine an estimated mass of at least a portion of the object by:
determining an estimated mass contribution of a first set of cubes contained entirely within the representation of the surface;
determining an estimated mass contribution of a second set of cubes having intersection points with the representation of the surface; and
estimating a mass of the at least a portion of the object based on the estimated mass contribution of the first set of cubes and the estimated mass contribution of the second set of cubes, wherein refining the initial representation comprises deforming the initial representation by iteratively applying local movement vectors to vertexes of a plurality of triangles defining the surface and a local movement vector u of a vertex of a triangle is defined by:
u=u_{1}+u_{2}+u_{3},
 The system of claim 1 wherein the object is one of a human brain and a human femur.

A system, comprising:
a memory; and
one or more processing devices configured to:
Mass=∑1nVbcda+∑1mVpdati, where n is a number of cubes in the first set of cubes, V_{bc }is a volume of a cube in the first set of cubes, d_{a }is an average density of the cube in the first set of cubes, m is a number of cubes in the second set of cubes, V_{p }is a volume of a portion of a cube in the second set of cubes contained within the surface, and d_{ati }is an average density of the portion of the cube in the second set of cubes.determine intersection points of a representation of a surface in an image space with cubes defining the image space, the surface defining a surface of an object; and
determine an estimated mass of at least a portion of the object by:
determining an estimated mass contribution of a first set of cubes contained entirely within the representation of the surface;
determining an estimated mass contribution of a second set of cubes having intersection points with the representation of the surface; and
estimating a mass of the at least a portion of the object based on the estimated mass contribution of the first set of cubes and the estimated mass contribution of the second set of cubes, wherein the one or more processing devices are configured to determine the estimated mass, Mass, of the at least a portion of the object according to:

The system of claim 3 wherein the one or more processing devices are configured to determine the intersection points by:
defining an initial representation of the surface in the image space;
refining the initial representation of the surface in the image space; and
determining the intersection points of the refined representation of the surface with the cubes defining the image space.
 The system of claim 4 wherein refining the initial representation of the surface in the image space comprises segmenting the object and refining the initial representation to correspond to a segmented portion of the object.
 The system of claim 5 wherein refining the initial representation comprises applying a marching cubes algorithm to the segmented portion of the object.
 The system of claim 5 wherein refining the initial representation comprises deforming the initial representation.

The system of claim 5 wherein the one or more processing devices are configured to process an image dataset associating vertexes of the cubes defining the image space with intensity values indicative of density and the defining the initial representation comprises:
determining a maximum intensity threshold based on the dataset;
determining a minimum intensity threshold based on the dataset; and
determining an intensity threshold based on the maximum and minimum intensity thresholds.
 The system of claim 8 wherein refining the initial representation comprises deforming the initial representation by iteratively applying local movement vectors to vertexes of a plurality of triangles defining the surface.

The system of claim 9 wherein a local movement vector u of a vertex of a triangle is defined by:
u=u_{1}+u_{2}+u_{3},
where u_{1 }is a vector component representing local movement within the surface, u_{2 }is a vector component representing local movement along surface normal, and u_{3 }is a vector component representing local movement deforming the surface toward a surface of the segmented object.  The system of claim 3 wherein determining the estimated mass contribution of a second set of cubes comprises representing a cube in the second set of cubes as a plurality of subcubes and V_{p }is a total volume of the cube in the second set of cubes multiplied by a ratio of a number of subcubes of the cube in the second set of cubes determined to be within the at least a portion of the object to a total number of subcubes of the cube in the second set of cubes.
 The system of claim 11 wherein the total number of subcubes in the cube is four.

A method, comprising:
determining intersection points of a representation of a surface in an image space with cubes defining the image space, the surface representing a surface of an object; and
estimating a mass of at least a portion of the object, the estimating including:
Mass=∑1nVbcda+∑1mVpdati, where n is a number of cubes in the first set of cubes, V_{bc }is a volume of a cube in the first set of cubes, d_{a }is an average density of the cube in the first set of cubes, m is a number of cubes in the second set of cubes, V_{p }is a volume of a portion of a cube in the second set of cubes contained within the surface, and d_{ati }is an average density of the portion of the cube in the second set of cubes.estimating a mass contribution of a first set of cubes contained entirely within the representation of the surface;
estimating a mass contribution of a second set of cubes having intersection points with the representation of the surface; and
estimating a mass of the at least a portion of the object based on the estimated mass contribution of the first set of cubes and the estimated mass contribution of the second set of cubes, wherein the estimated mass of the at least a portion of the object is:

The method of claim 13 wherein determining the intersection points comprises:
defining an initial representation of the surface in the image space;
refining the initial representation of the surface in the image space; and
determining the intersection points of the refined representation of the surface with the cubes defining the image space.
 The method of claim 14 wherein refining the initial representation of the surface in the image space comprises segmenting the object and refining the initial representation to correspond to a segmented portion of the object.
 The method of claim 14 wherein refining the initial representation comprises deforming the initial representation.
 The method of claim 13 comprising processing an image dataset associating vertexes of the cubes defining the image space with intensity values indicative of density.
 The method of claim 13 comprising applying a marching cubes algorithm.
 The method of claim 13 wherein estimating the mass contribution of a second set of cubes comprises representing a cube in the second set of cubes as a plurality of subcubes and V_{p }is a total volume of the cube in the second set of cubes multiplied by a ratio of a number of subcubes of the cube in the second set of cubes determined to be within the at least a portion of the object to a total number of subcubes of the cube in the second set of cubes.

A nontransitory computerreadable memory containing instructions configured to cause a processing device to estimate a mass of an object by performing a method, the method comprising:
determining intersection points of a representation of a surface in an image space with cubes defining the image space, the surface representing a surface of an object; and
estimating a mass of at least a portion of the object, the estimating including:
Mass=∑1nVbcda+∑1mVpdati, where n is a number of cubes in the first set of cubes, V_{bc }is a volume of a cube in the first set of cubes, d_{a }is an average density of the cube in the first set of cubes, m is a number of cubes in the second set of cubes, V_{p }is a volume of a portion of a cube in the second set of cubes contained within the surface, and d_{ati }is an average density of the portion of the cube in the second set of cubes.estimating a mass contribution of a first set of cubes contained entirely within the representation of the surface;
estimating a mass contribution of a second set of cubes having intersection points with the representation of the surface; and
estimating a mass of the at least a portion of the object based on the estimated mass contribution of the first set of cubes and the estimated mass contribution of the second set of cubes, wherein the estimated mass of the at least a portion of the object is:

The nontransitory computerreadable medium of claim 20 wherein determining the intersection points comprises:
defining an initial representation of the surface in the image space;
refining the initial representation of the surface in the image space; and
determining the intersection points of the refined representation of the surface with the cubes defining the image space.
 The nontransitory computerreadable medium of claim 21 wherein refining the initial representation comprises deforming the initial representation.

A system, comprising:
a memory; and
one or more processing devices configured to:
define an initial representation of a surface in an image space having cubes defining the image space, the surface representing a surface of an object; and
deform the initial representation of the surface by iteratively applying local movement vectors to vertexes of a plurality of triangles defining the surface, wherein a local movement vector u of a vertex of a triangle is defined by:
u=u_{1}+u_{2}+u_{3},
where u_{1 }is a vector component representing local movement within the surface, u_{2 }is a vector component representing local movement along surface normal, and u_{3 }is a vector component representing local movement deforming the surface toward a surface of the segmented object.

The system of claim 23 wherein the one or more processing devices are configured to:
segment the object in the image space;
refine the initial representation to correspond to a segmented portion of the object; and
apply a marching cubes algorithm to the refined representation.

A method, comprising:
defining an initial representation of a surface in an image space having cubes defining the image space, the surface representing a surface of an object; and
deforming the initial representation of the surface by iteratively applying local movement vectors to vertexes of a plurality of triangles defining the surface, wherein a local movement vector u of a vertex of a triangle is defined by:
u=u_{1}+u_{2}+u_{3},
where u_{1 }is a vector component representing local movement within the surface, u_{2 }is a vector component representing local movement along surface normal, and u_{3 }is a vector component representing local movement deforming the surface toward a surface of the segmented object.

The method of claim 25 comprising:
segmenting the object in the image space;
refining the initial representation to correspond to a segmented portion of the object; and
applying a marching cubes algorithm to the refined representation.
 The method of claim 25 wherein the object is one of a human brain and a human femur.

A system, comprising:
means for defining an initial representation of a surface in an image space having cubes defining the image space, the surface representing a surface of an object;
means for iteratively applying local movement vectors to vertexes of a plurality of triangles defining the surface to produce a refined representation of the surface; and
means for estimating a mass of the object based on the refined representation of the surface, wherein a local movement vector u of a vertex of a triangle is defined by:
u=u_{1}+u_{2}+u_{3},
where u_{1 }is a vector component representing local movement within the surface, u_{2 }is a vector component representing local movement along surface normal, and u_{3 }is a vector component representing local movement deforming the surface toward a surface of the segmented object.
Owners (US)

Stmicroelectronics S.r.l
(Jan 08 2013)
Explore more patents:
Applicants

St Microelectronics Srl
Explore more patents:
Inventors

Pau Danilo Pietro
Explore more patents:

Masala Daniele
Explore more patents:

Bao Xinfeng
Explore more patents:
CPC Classifications

G06T17/00
Explore more patents:

G06T2207/10072
Explore more patents:

G06T2207/30016
Explore more patents:

G06T2210/21
Explore more patents:

G06T2210/41
Explore more patents:

G06T7/00
Explore more patents:

G06T7/0012
Explore more patents:

G06T7/62
Explore more patents:
Document Preview
 Publication: Sep 29, 2015

Application:
Dec 26, 2012
US 201213727483 A

Priority:
Dec 26, 2012
US 201213727483 A

Priority:
Dec 23, 2011
US 201161630989 P