PIXEL CLASSIFICATION IN IMAGE ANALYSIS

• US 20090214114A1
• Filed: 02/19/2009
• Published: 08/27/2009
• Est. Priority Date: 02/19/2008
• Status: Abandoned Application

• Current Assignee: Diascan AB
• Original Assignee: Diascan AB
• Inventors: LINDBLAD, Joakim, WAHLBY, Carolina, GAVRILOVIC, Milan, BENGTSSON, Evert
• Application Number:
  US12/388,577
• Publication Number:
  US 20090214114A1
• Time in Patent Office: Days
• US Class Current: 382/170
• CPC Class Codes: G01N 21/6458 Fluorescence microscopy fluorescence microscopes per se G02B21/0076 and G02B21/16
• CPC Class Codes: G06V 10/758 Involving statistics of pixels or of feature values, e.g. histogram matching
• CPC Class Codes: G06V 20/698 Matching; Classification

Abstract

A method for classifying image pixels comprises obtaining (210) of a plurality of pixel vectors of an image. Each said pixel vector has n intensity elements associated with a same respective imaged position, n≧2. Each of the n intensity elements is a digital value representing a discretized intensity measure of light, within a respective predetermined wavelength interval, coming from the imaged position. The method further comprises creating (220) of an angle histogram of angles of the pixel vectors, in a space spanned by unity vectors of the n intensity elements. At least one angle interval is defined (230), corresponding to a respective pixel class, based on statistics of the angle histogram. Pixel vectors are classified based on the defined angle intervals. Co-localization classification insensitive to cross-talk can thereby be obtained. A fluorescence microscopy device having an image analyser according to the method is presented.

Claims

1. Method for classifying image pixels, comprising the step of:

obtaining a plurality of pixel vectors of an image,

each said pixel vector having n intensity elements associated with a same respective imaged position, where n≧

2;

each of said n intensity elements being a digital value representing a discretized intensity measure of light, within a respective predetermined wavelength interval, coming from said imaged position;

classifying pixel vectors based on at least one angle interval;

creating an angle histogram, in n−

1 dimensions, of angles, in a space spanned by unity vectors of said n intensity elements, of said pixel vectors, said angles being defined relative to said unity vectors; and

defining said at least one angle interval corresponding to a respective pixel class, based on statistics of said angle histogram in n−

1 dimensions.

2. Method according to claim 1, wherein said step of creating comprises compensation of said angle histogram for discretization noise.

3. Method according to claim 1, wherein said image is a fluorescence microscopy image.

4. Method according to claim 1, wherein said step of creating further comprises weighting a contribution of said angles to said histogram by a factor being a function of a length of the respective pixel vector.

5. Method according to claim 4, wherein said factor is proportional to a maximum length of projections of respective pixel vector onto said unity vectors of said n intensity elements.

6. Method according to claim 4, wherein said factor is proportional to a Euclidian length of respective said pixel vector.

7. Method according to claim 2, wherein said compensation of said angle histogram for discretization noise comprises smearing of a contribution of a pixel vector to said histogram between histogram bins representing angles falling within discretization uncertainty from respective said pixel vector.

8. Method according to claim 2, wherein said step of creating an angle histogram comprises smoothing of said angle histogram.

9. Method according to claim 1, wherein said plurality of pixel vectors representing imaged positions of an image of at least one spatial dimension.

10. Method according to claim 1, wherein said plurality of pixel vectors representing discretized intensity measures obtained at different time instances.

11. Method according to claim 1, wherein said at least one angle intensity interval comprises at least one fuzzy angle interval.

12. Method according to claim 1, wherein one said pixel class is associated with co-localization.

13. Method according to claim 1, wherein said step of defining at least one angle interval corresponding to a respective pixel class comprises:

identifying at least one distinct angle range in said histogram having generally higher amplitudes than surrounding angle ranges; and

defining said angle intervals to encompass a respective said distinct angle range.

14. Method according to claim 13, wherein said defining of said angle intervals comprises:

selecting a representative angle in each said at least two distinct angle ranges; and

defining borders between neighbouring said at least two distinct angle ranges to cross a middle point of a connection line between a pair of neighbouring representative angles.

15. Method according to claim 1, further comprising having one class corresponding to each of a pure intensity element and by:

selecting a representative pure element angle in each distinct angle range corresponding to a respective pure intensity element; and

quantifying a cross-talk between said pure intensity elements as an angle difference between said representative pure element angle and corresponding intensity element axis.

16. Method according to claim 15, further comprising compensating cross-talk based on said cross-talk quantifications.

17. Method according to claim 1, further comprising:

selecting a representative pure element angle in each distinct angle range corresponding to a respective pure intensity element; and

transforming said pixel vector to be expressed as linear combinations of said representative pure element angle vectors;

performing pixel vector classification on said transformed pixel vectors.

18. Method according to claim 1, further comprising classifying pixel vectors having a small length as background.

19. Method according to claim 1, further comprising performing cluster analysis on classified pixel vectors and classifying pixel vectors falling outside said clusters as background.

20. Fluorescence microscopy device, comprising:

a fluorescence microscope, providing an image of a sample;

intensity measurement means arranged to determine a digital value of discretized intensity measures, within at least two predetermined wavelength intervals, of light coming from an imaged position; and

an image analyser connected to said intensity measurement means, said image analyser comprising;

means for obtaining a plurality of pixel vectors from said intensity measurement means,

each said pixel vector having n intensity elements associated with a same respective imaged position, where n≧

2;

said n intensity elements representing said determined digital values;

means for creating an angle histogram, in n−

1 dimensions, of angles, in a space spanned by unity vectors of said n intensity elements, of said pixel vectors, said angles being defined relative to said unity vectors;

means for defining at least one angle interval corresponding to a respective pixel class, based on said angle histogram in n−

1 dimensions;

means for classifying pixel vectors in a corresponding said pixel class based on said at least one angle interval; and

means for outputting said classifying of said pixel vectors.