System and Method for Detecting Spherical and Ellipsoidal Objects Using Cutting Planes

• US 20090016583A1
• Filed: 07/09/2008
• Published: 01/15/2009
• Est. Priority Date: 07/10/2007
• Status: Abandoned Application

• Current Assignee: Siemens Medical Solutions USA Incorporated (Siemens AG)
• Original Assignee: Siemens Medical Solutions USA Incorporated (Siemens AG)
• Inventors: Lakare, Sarang, Salganicoff, Marcos, Wolf, Matthias
• Application Number:
  US12/169,773
• Publication Number:
  US 20090016583A1
• Time in Patent Office: Days
• US Class Current: 382/128
• CPC Class Codes: G06V 10/421 by analysing segments intersecting the pattern
• CPC Class Codes: G06V 10/443 by matching or filtering
• CPC Class Codes: G06V 2201/032 of protuberances, polyps nodules, etc.

Abstract

A method for detecting spherical and ellipsoidal objects is digitized medical images includes providing a 2-dimensional (2D) slice I(x, y) extracted from a medical image volume of a colon, said image volume comprising a plurality of intensities associated with a 3 grid of points, generating a plurality of templates of different sizes whose shape matches a target structure being sought in said slice, calculating a normalized gradient from said slice, calculating a diverging field gradient response (DFGR) for each of the plurality of masks with the normalized gradient, and selecting a strongest response as being indicative of the position and size of the target structure.

Claims

1. A method for detecting spherical and ellipsoidal objects is digitized medical images comprising the steps of:

providing a 2-dimensional (2D) slice I(x, y) extracted from a medical image volume of a colon, said image volume comprising a plurality of intensities associated with a 3D grid of points;

separating the colon from other structures in the slice by analyzing partial volume artifacts; and

finding a target structure in said slice.

2. The method of claim 1, further comprising:

generating a plurality of templates of different sizes whose shape matches a target structure being sought in said slice;

calculating a normalized gradient from said slice;

calculating a diverging field gradient response (DFGR) for each of the plurality of masks with the normalized gradient; and

selecting a strongest response as being indicative of the position and size of the target structure.

3. The method of claim 1, wherein said 2D slice is extracted from said image volume using a cutting plane.

4. The method of claim 1, wherein said structure being sought is a polyp in an image volume of a colon.

5. The method of claim 2, wherein calculating a diverging field gradient response comprises calculating

$\sum _{}$

j ∈

Ω



∑

i ∈

Ω



M x 

( i , j ) 

I x 

( x - i , y - j ) + ∑

j ∈

Ω



∑

i ∈

Ω



M y 

( i , j ) 

I y 

( x - i , y - j ) ,

wherein I_x and I_y are the normalized gradients of slice I(x, y), M_x(i,j)=i/√

{square root over (i²+j²)}, M_y(i,j)=j/√

{square root over (i²+j²)}, is a mask vector of size S, and Ω

=[−

floor(S/2), floor (S/2)].

6. The method of claim 1, The method of claim 1, further comprising:

considering each point in said slice and a center and counting a number of points within a given radius of each said center point that fulfill a predetermined selection criteria;

providing an accumulator array indexed by center point coordinates and radii values;

incrementing an accumulator value by the number of points found to fulfill said criteria; and

finding a peak in said accumulator array, wherein the indices of said peak value are indicative of a center and radius of a target structure in said slice.

7. The method of claim 1, further comprising:

selecting a first starting point in said slice;

selecting a nearest neighbor point of said starting point having a least intensity value, and selecting said nearest neighbor point as a new starting point;

repeating said step of selecting a nearest neighbor point of said starting point having a least intensity value, and selecting said nearest neighbor point as a new starting point until a point with a minimal intensity is reached wherein said selected starting points form a path from said first starting point to said minimal intensity point; and

repeating said steps of selecting a first starting point, selecting a nearest neighbor point of said starting point, and repeating said steps for each point in said slice not already on a path of starting points, wherein said paths of starting points define disjoint regions in said slice indicative of structures in said slice.

8. The method of claim 1, further comprising:

calculating a texture feature value for each point in said slice over a window about each point;

using said texture feature values to classify points;

merging adjacent points with a same classification in to a same region;

wherein a region is indicative of structures in said slice.

9. The method of claim 8, wherein said texture features are calculated from one of intensity values, color values, or derived image quantities.

10. The method of claim 8, wherein said texture features include one or more of Haralick coefficients, co-occurrence matrices, local masks, and moments-based features.

11. A program storage device readable by a computer, tangibly embodying a program of instructions executable by the computer to perform the method steps for detecting spherical and ellipsoidal objects is digitized medical images, said method comprising the steps of:

providing a 2-dimensional (2D) slice I(x, y) extracted from a medical image volume of a colon, said image volume comprising a plurality of intensities associated with a 3D grid of points;

separating the colon from other structures in the slice by analyzing partial volume artifacts; and

finding a target structure in said slice.

12. The computer readable program storage device of claim 11, the method further comprising:

generating a plurality of templates of different sizes whose shape matches a target structure being sought in said slice;

calculating a normalized gradient from said slice;

calculating a diverging field gradient response (DFGR) for each of the plurality of masks with the normalized gradient; and

selecting a strongest response as being indicative of the position and size of the target structure.

13. The computer readable program storage device of claim 11, wherein said 2D slice is extracted from said image volume using a cutting plane.

14. The computer readable program storage device of claim 11, wherein said structure being sought is a polyp in an image volume of a colon.

15. The computer readable program storage device of claim 12, wherein calculating a diverging field gradient response comprises calculating

$\sum _{}$

j ∈

Ω



∑

i ∈

Ω



M x 

( i , j ) 

I x 

( x - i , y - j ) + ∑

j ∈

Ω



∑

i ∈

Ω



M y 

( i , j ) 

I y 

( x - i , y - j ) ,

wherein I_x and I_y are the normalized gradients of slice I(x, y), M_x(i,j)=i/√

{square root over (i²+j²)}, M_y(i,j)=j/√

{square root over (i²+j²)}, is a mask vector of size S, and Ω

=[−

floor(S/2), floor (S/2)].

16. The computer readable program storage device of claim 11, the method further comprising:

considering each point in said slice and a center and counting a number of points within a given radius of each said center point that fulfill a predetermined selection criteria;

providing an accumulator array indexed by center point coordinates and radii values;

incrementing an accumulator value by the number of points found to fulfill said criteria; and

finding a peak in said accumulator array, wherein the indices of said peak value are indicative of a center and radius of a target structure in said slice.

17. The computer readable program storage device of claim 11, the method further comprising:

selecting a first starting point in said slice;

selecting a nearest neighbor point of said starting point having a least intensity value, and selecting said nearest neighbor point as a new starting point;

repeating said step of selecting a nearest neighbor point of said starting point having a least intensity value, and selecting said nearest neighbor point as a new starting point until a point with a minimal intensity is reached wherein said selected starting points form a path from said first starting point to said minimal intensity point; and

repeating said steps of selecting a first starting point, selecting a nearest neighbor point of said starting point, and repeating said steps for each point in said slice not already on a path of starting points, wherein said paths of starting points define disjoint regions in said slice indicative of structures in said slice.

18. The computer readable program storage device of claim 11, the method further comprising:

calculating a texture feature value for each point in said slice over a window about each point;

using said texture feature values to classify points;

merging adjacent points with a same classification in to a same region;

wherein a region is indicative of structures in said slice.

19. The computer readable program storage device of claim 18, wherein said texture features are calculated from one of intensity values, color values, or derived image quantities.

20. The computer readable program storage device of claim 18, wherein said texture features include one or more of Haralick coefficients, co-occurrence matrices, local masks, and moments-based features.

21. A method for detecting spherical and ellipsoidal objects is digitized medical images comprising the steps of:

providing a 2-dimensional (2D) slice I(x, y) extracted from a medical image volume of a colon, said image volume comprising a plurality of intensities associated with a 3D grid of points;

generating a plurality of templates of different sizes whose shape matches a target structure being sought in said slice;

calculating a normalized gradient from said slice;

calculating a diverging field gradient response (DFGR) for each of the plurality of masks with the normalized gradient; and

selecting a strongest response as being indicative of the position and size of the target structure.

22. The method of claim 21, further comprising separating the colon from other structures in the slice by analyzing partial volume artifacts.