BLOOD VESSELS ANALYSIS METHODOLOGY FOR THE DETECTION OF RETINA ABNORMALITIES

1 . A method for detecting abnormalities or suspicious artifacts in a retinal image, the method comprising:

acquiring data features from the retinal image;

mapping a structure of a retinal blood vessel network from the retinal image;

analyzing said structure to compute blood vessel features; and

analyzing a set of said data features from the retinal image vis-à-vis a corresponding set of said blood vessel features.

2 . The method of claim 1 , wherein said retinal image is pre-processed prior to extracting said data features.

3 . The method of claim 2 , wherein said retinal image is pre-processed by at least one pre-processing method selected from the group comprising: re-sampling, normalization and contrast-limited adaptive histogram equalization.

4 . The method of claim 1 , wherein said structure of blood vessels is extracted by:

(i) transforming the retinal image into a grayscale image, and

(ii) applying Principal Component Analysis (PCA) to said grayscale image.

5 . The method of claim 1 , wherein said structure of blood vessels is extracted by:

(i) applying non-linear edge detection to the retinal image.

6 . The method of claim 5 , wherein Kirsch's templates are used for said non-linear edge detection.

7 . The method of claim 1 , wherein said structure of blood vessels is extracted by:

(i) transforming the retinal image into a grayscale image,

(ii) applying Principal Component Analysis (PCA) to said grayscale image, and

(iii) applying non-linear edge detection to said grayscale image.

8 . The method of claim 1 , further comprising:

extracting data features related to local retina properties from said retinal image.

9 . The method of claim 8 , wherein said step of extracting data features is performed prior to said step of analyzing said extracted structure of blood vessels.

10 . The method of claim 1 , wherein said data features are acquired by:

(i) color resampling of the retinal image to 3 dimensional 30×30×30 color buckets for each 1×1, 2×2, 4×4 and 8×8 areas of the retinal image;

(ii) applying transformation of color buckets to a scalar used RGB color mapping; and

(iii) adding relational features to describe bucket distribution of colors in neighborhood of each said 8×8 area.

11 . The method of claim 8 , further comprising:

analyzing features of said extracted structure of blood vessels vis-à-vis said extracted data features related to said local retina properties.

12 . The method of claim 11 , further comprising:

feeding computed features from said analysis of said data features vis-à-vis said blood vessel features to a machine learning mechanism or statistical model.

13 . A method for training a learning machine on retinal images, the method comprising:

mapping a structure of a blood vessel network from a retinal image;

analyzing said structure to compute blood vessel features; and

training the learning machine with said blood vessel features.

14 . The method of claim 13 , further comprising:

acquiring data features from said retinal image; and

analyzing a set of said data features vis-à-vis a corresponding set of said blood vessel features.