METHOD OF COMBINING BINARY CLUSTER MAPS INTO A SINGLE CLUSTER MAP

• US 20090324035A1
• Filed: 07/17/2007
• Published: 12/31/2009
• Est. Priority Date: 08/02/2006
• Status: Abandoned Application

• Current Assignee: Koninklijke Philips Electronics N.V. (Koninklijke Philips N.V.)
• Original Assignee: Koninklijke Philips Electronics N.V. (Koninklijke Philips N.V.)
• Inventors: Wengler, Mark Christof, Paulus, Timo, Fischer, Alexander
• Application Number:
  US12/375,747
• Publication Number:
  US 20090324035A1
• Time in Patent Office: Days
• US Class Current: 382/128
• CPC Class Codes: G06F 18/23 Clustering techniques
• CPC Class Codes: G06F 18/254 of classification results, e.g. of results related to same input data
• CPC Class Codes: G06F 18/256 of results relating to different input data, e.g. multimodal recognition
• CPC Class Codes: G06F 18/40 Software arrangements specially adapted for pattern recognition, e.g. user interfaces or toolboxes therefor
• CPC Class Codes: G06T 2207/10072 Tomographic images
• CPC Class Codes: G06T 2207/30004 Biomedical image processing
• CPC Class Codes: G06T 7/0012 Biomedical image inspection
• CPC Class Codes: G06T 7/11 Region-based segmentation
• CPC Class Codes: G06T 7/143 involving probabilistic approaches, e.g. Markov random field [MRF] modelling
• CPC Class Codes: G06V 10/762 using clustering, e.g. of similar faces in social networks
• CPC Class Codes: G06V 10/809 of classification results, e.g. where the classifiers operate on the same input data
• CPC Class Codes: G06V 10/811 the classifiers operating on different input data, e.g. multi-modal recognition
• CPC Class Codes: G06V 10/945 User interactive design; Environments; Toolboxes

Abstract

This invention relates to a method of combining multiple binary cluster maps into a single cluster map; where each respective binary cluster map represents characteristic information and the single cluster map represent the sum of the characteristic information. Initially, each respective binary cluster map is assigned with a reliability factor for indicating the reliability of the binary cluster map. These factor values are then used to determine a reliability vector comprising reliability factor elements, where each respective reliability factor element is associated to certain cluster map area in the single cluster map and indicates the reliability of cluster map are. In that way, the single cluster map can be viewed with respect to the reliability.

Claims

1. A method of combining multiple binary cluster maps (201-203) into a single cluster map (301), where each respective binary cluster map includes characteristic information and the single cluster map represents a combination of the characteristic information, the method comprising:

assigning (101) each respective binary cluster map (201-203) with a reliability factor for indicating the reliability of each respective binary cluster map,

utilizing (103) the reliability factors as input parameters for a pre-defined combination rule for determining a reliability vector (302) for the single cluster map, wherein the reliability vector comprises reliability factor elements (304, 306, 308), where each respective reliability factor element is associated to a certain cluster map area (303, 307, 201) in the single cluster map and indicates the reliability of the cluster map area.

2. A method according to claim 1, wherein the method further comprises:

assigning (105) a threshold value for the reliability vector (302), and

utilizing (107) the assigned threshold value as an input parameter for an updated single cluster map (401, 501, 601).

3. A method according to claim 1, wherein the step of assigning each respective binary cluster map (201-203) with a reliability factor is performed manually.

4. A method according to claim 1, wherein the step of assigning each respective binary cluster map (201-203) with a reliability factor is performed automatically by comparing the multiple binary clusters with reference binary clusters having assigned reliability factors.

5. A method according to claim 2, wherein the step of assigning a threshold value for the reliability factor elements (304, 306, 308) is performed manually.

6. A method according to claim 2, wherein the step of assigning a threshold value for the reliability factor elements (304, 306, 308) is performed automatically by comparing the multiple binary clusters with reference binary clusters having assigned threshold values.

7. A method according to claim 1, wherein the pre-defined combination rule is given by the equation:

R<sub>N,N−

1, . . . , 1</sub>R_N+(1−

R_N)R<sub>N−

1, . . . , 1</sub>

with Rj as the reliability factor for binary cluster map j=1 . . . N, where N is the total number of initial binary cluster maps.

8. A method according to claim 2, wherein different color information is associated to each respective binary cluster map (201-203), and wherein the reliability vector (302) is displayed simultaneously with the combined cluster map (301) with corresponding color information such that each vector element (304, 306, 308) associated with a given combined cluster map portion is displayed with the same color information.

9. A computer program product for instructing a processing unit to execute the method step of claim 1 when the product is run on a computer.

10. A device (700) adapted to combine multiple binary cluster maps (201-203) into a single cluster map (301), where each respective binary cluster map includes characteristic information and the single cluster map represents a combination of the characteristic information, comprising:

assigning unit (702) for assigning each respective binary cluster map (201-203) with a reliability factor for indicating the reliability of each respective binary cluster map, and

a processor (703) for utilizing the reliability factors as input parameters for a pre-defined combination rule for determining a reliability vector (302) for the single cluster map, wherein the reliability vector comprises reliability factor elements (304, 306, 308), where each respective reliability factor element is associated to a certain cluster map area (303, 307, 201) in the single cluster map and indicates the reliability of the cluster map area.

11. A device according to claim 10, wherein the assigning unit (702) for assigning comprises an input unit adapted to receive a manual input from a user (701) or an algorithm adapted to automatically evaluate the reliability factor assigned to each respective binary cluster map.

12. A device according to claim 10, being comprised in a medical workstation or medical imaging system.