Method, an Apparatus and a Computer Program For Segmenting an Anatomic Structure in a Multi-Dimensional Dataset

• US 20080205724A1
• Filed: 04/11/2006
• Published: 08/28/2008
• Est. Priority Date: 04/12/2005
• Status: Abandoned Application

• Current Assignee: Koninklijke Philips Electronics N.V. (Koninklijke Philips N.V.)
• Original Assignee: Koninklijke Philips N.V.
• Inventors: Netsch, Thomas, Cocosco, Christian Adrian, Niessen, Wiro Joep
• Application Number:
  US11/911,216
• Publication Number:
  US 20080205724A1
• Time in Patent Office: Days
• US Class Current: 382/130
• CPC Class Codes: G06T 2207/10072 Tomographic images
• CPC Class Codes: G06T 2207/20092 Interactive image processing based on input by user
• CPC Class Codes: G06T 2207/20132 Image cropping
• CPC Class Codes: G06T 2207/30048 Heart; Cardiac
• CPC Class Codes: G06T 7/0012 Biomedical image inspection
• CPC Class Codes: G06T 7/11 Region-based segmentation
• CPC Class Codes: G06V 10/248 by interactive preprocessing or interactive shape modelling, e.g. feature points assigned by a user
• CPC Class Codes: G06V 10/267 by performing operations on regions, e.g. growing, shrinking or watersheds
• CPC Class Codes: G06V 2201/03 Recognition of patterns in medical or anatomical images

Abstract

The method 1 according to the invention is preferably practiced in real time and directly after a suitable acquisition 3 of the multi-dimensional dataset, which is accessed at step 5 and the images constituting the multi-dimensional dataset are classified at step 8. Preferably, for reducing an amount of data to be processed at step 6 the image data is subjected to a restrictive region of interest determination. At step 9 the classified cardiac images are subjected to a an image thinning operator so that the resulting images comprise a plurality of connected image components which are further analyzed at step 14. After the thinning step 9 a labeling step 11 is performed, where different connected components in the multi-dimensional dataset are accordingly labeled. This step is preferably followed by a region growing step 13, which is constrained by binary threshold used at step 8b. For each connected image component a factor F is computed at step 14. The anatomic structure is segmented at step 16 by selecting the connected image component with factor F meeting a pre-determined criterion. After this, the segmented anatomic structure is stored in a suitable format at step 18. The invention further relates to an apparatus, a working station, a viewing station and a computer program.

Claims

1. A method for segmenting an anatomic structure in a multi-dimensional dataset comprising a plurality of temporally spaced cardiac images comprising data on a target matter and on an other matter, said method comprising the following steps:

performing a classification of cardiac images to distinguish between the target matter and the other matter yielding classified cardiac images comprising the target matter;

applying a thinning operator to the classified cardiac images yielding processed cardiac images comprising connected image components;

labeling different connected image components yielding respective labeled connected image components;

comprising;

computing, for each labeled connected image component, a factor based on a difference between a first volume of the connected image component in a first cardiac image of said cardiac images and a second volume of the connected image component in a second cardiac image of said cardiac images; and

segmenting the anatomic structure by selecting the connected image component with the factor meeting a predetermined criterion.

2. A method according to claim 1, said method further comprising a preparatory step of:

automatically computing a restrictive region of interest around the heart in the cardiac images of the multi-dimensional dataset.

3. A method according to claim 1, whereby the method further comprises the steps of:

performing a region growing operation for the multi-dimensional dataset, whereby said region growing operation is being constrained by a parameter deduced from the classified cardiac images.

4. A method according to claim 1, whereby a further anatomic structure is conceived to be segmented in the cardiac images, said method further comprising the steps of:

applying a thinning operator to the classified cardiac images yielding processed cardiac images comprising further connected image components;

labeling different further connected image components yielding respective labeled further connected image components;

computing, for each labeled further connected image component, a further factor based on a difference between a first volume of the further connected image component in a first cardiac image of said cardiac images and a second volume of the further connected image component in a second cardiac image of said cardiac images; and

segmenting the further anatomic structure by selecting the further connected image component with the value of said further factor meeting a further pre-determined criterion.

5. A method according to claim 4, further comprising the step of:

segmenting a still further anatomic structure based on a comparison between the segmented anatomic structure and the segmented further anatomic structure.

6. A method according to claim 4, further comprising the step of:

computing a still further factor based on a ratio (F1/F2) between the factor (F1) and the further factor (F2);

comparing a value of the still further factor with a still further pre-determined criterion;

performing an automatic correction of a stack of cardiac images upon an event that the still further factor and the criterion inter-relate in a pre-determined way.

7. A method according to claim 1, said method further comprising the step of:

visualizing the at least any one of the segmented anatomic structure, the segmented further anatomic structure and the segmented still further anatomic on a display means.

8. An apparatus for segmenting an anatomic structure in a multi-dimensional dataset comprising a plurality of temporally spaced cardiac images comprising data on a target matter and on an other matter, said apparatus comprising:

an input for accessing the multi-dimensional dataset;

a computing unit for;

i. performing a classification of cardiac images to distinguish between the target matter and the other matter yielding classified cardiac images comprising the target matter;

ii. applying a thinning operator to the classified cardiac images yielding processed cardiac images comprising connected image components;

iii. labeling different connected image components yielding respective labeled connected image components

iv. Computing, for each labeled connected image component, a factor based on a difference between a first volume of the connected image component in a first cardiac image of said cardiac images and a second volume of the connected image component in a second cardiac image of said cardiac; and

v. segmenting the anatomic structure by selecting the connected image component with the factor meeting a pre-determined criterion.

9. An apparatus according to claim 8, whereby the apparatus further comprises a display unit for displaying the segmented anatomic structure.

10. An apparatus according to claim 8, whereby the apparatus further comprises:

a data acquisition unit arranged to acquired the multi-dimensional dataset.

11. A working station comprising an apparatus according to claim 8.

12. A viewing station comprising an apparatus according to claim 9.

13. A computer program for segmenting an anatomic structure in a multi-dimensional dataset comprising a plurality of temporally spaced cardiac images comprising data on a target matter and on an other matter, said computer program comprising instruction to cause a processor to carry out the following steps:

performing a classification of cardiac images to distinguish between the target matter and the other matter yielding classified cardiac images comprising the target matter;

applying a thinning operator to the classified cardiac images yielding processed cardiac images comprising connected image components;

labeling different connected image components yielding respective labeled connected image components;

for each labeled connected image component compute a factor based on a difference between a first volume of the connected image component in a first cardiac image of said cardiac images and a second volume of the connected image component in a second cardiac image of said cardiac images; and

segmenting the anatomic structure by selecting the connected image component with the factor meeting a pre-determined criterion.