OPTIMISED REGION OF INTEREST SELECTION

• US 20100232671A1
• Filed: 12/15/2009
• Published: 09/16/2010
• Est. Priority Date: 12/17/2008
• Status: Active Grant

• Current Assignee: BioClinica, Inc.
• Original Assignee: Nordic Bioscience Imaging A/S
• Inventors: Qazi, Arish, Jørgenson, Dan Richter, Dam, Erik B., Lillholm, Martin, Nielsen, Mads
• Granted Patent: US 8,538,102 B2
• Time in Patent Office: Days
• US Class Current: 382/132
• CPC Class Codes: G06F 18/2113 by ranking or filtering the set of features, e.g. using a measure of variance or of feature cross-correlation
• CPC Class Codes: G06F 18/2132 based on discrimination criteria, e.g. discriminant analysis
• CPC Class Codes: G06V 10/771 Feature selection, e.g. selecting representative features from a multi-dimensional feature space
• CPC Class Codes: G06V 2201/03 Recognition of patterns in medical or anatomical images

Abstract

A statistical framework for identifying regions of discrimination between two groups of imaged objects involves determining a weight map defining the regions of discrimination by minimizing the sample size required to discriminate the two groups in a numerical optimization scheme.

Claims

1. A method for identifying a region of interest (ROI) in an object from a set of similar images of different instances of a said object, said method comprising:

using a suitably programmed computer to compute a weighted map of a feature of said image in which the weight of said feature at each location in said map is calculated to minimise the sample size needed to distinguish a first group of said examples from a second said group, said ROI consisting of the set of locations in said map at which said feature has the highest weights.

2. A method as claimed in claim 1, comprising the preliminary steps of segmenting each image and spatially aligning the segmented images.

3. A method as claimed in claim 1, wherein the weight of said feature at each location in the map is restricted to positive values.

4. A method as claimed in claim 1, wherein the weight of said feature at each location in the map is the absolute value of a positive or negative weight.

5. A method as claimed in claim 1, said calculation to minimise sample size is conducted by minimising for each location in said map a function indicative of sample size incorporating a penalty term which is a measure of the smoothness of the weighted map at said location.

6. A method as claimed in claim 1, said calculation to minimise sample size is conducted by minimising for each location in said map a function indicative of sample size incorporating a penalisation term which provides sparsity in the weighting of said feature at locations in said map.

7. A method as claimed in claim 4, wherein said penalisation term is substantially equivalent in its effect to a least absolute shrinkage and selection operator.

8. A method as claimed in claim 4, wherein said calculation to minimise sample size is carried out by computation of a functional to produce results substantially equivalent to computing the functional:

$\begin{array}{cc}\underset{w_1}{\mathrm{argmin}}<br><br>\underset{j\in <br><br>\left\{1,2\right\}}{\sum <br><br>}<br><br>\frac{\underset{k\in <br><br>G_j}{\sum <br><br>}<br><br>{\left({\tilde{F}}_j^k<br><br>\left[W\right]-{\widetilde{\mathrm{\mu <br><br>}}}_j\right)}^2}{n_j}+\mathrm{\lambda <br><br>}<br><br>{<br><br>\nabla <br><br>W<br><br>}_1+\mathrm{\eta <br><br>}<br><br>{<br><br>W<br><br>}_1& \left(7\right)\end{array}$

in which λ and

η

are optimised parameters, subject to the constraints W≧

0, Σ

W=1.

9. A method as claimed in claim 1, wherein said images are of biological objects.

10. A method as claimed in claim 9, wherein said images are of joint cartilages, bone, or brain, or are mammographic images.

11. A method as claimed in claim 9, wherein said images are of joint cartilages and said weighted map is a map weighting locations in said cartilages at which thickness or volume of the cartilage distinguishes cartilages impaired by arthritis from non-impaired cartilages.

12. A computer programmed to conduct a computation for identifying a region of interest (ROI) in an object from an input comprising a set of similar images of different instances of a said object, said computation producing a weighted map of a feature of said image in which the weight of said feature at each location in said map is calculated to minimise the sample size needed to distinguish a first group of said examples from a second said group, said ROI consisting of the set of locations in said map at which said feature has the highest weights.

13. An instruction set for programming a computer to conduct a computation for identifying a region of interest (ROI) in an object from an input comprising a set of similar images of different instances of a said object, said computation producing a weighted map of a feature of said image in which the weight of said feature at each location in said map is calculated to minimise the sample size needed to distinguish a first group of said examples from a second said group, said ROI consisting of the set of locations in said map at which said feature has the highest weights.