M. Elena Martinez-Perez , Alun D. Hughes , Simon A. Thom , Anil A. Bharath , Kim H. Parker

Medical Image Analysis 11 (2007) 47–61

黃銘哲 2010/11/9

1

Outline

1.Introduction

2.Method

3.Result

4.Validation

5.Conclusions

2

Introduction

The eye is a window to the retinal vascular system which is uniquely accessible for the non-invasive, in vivo study of a continuous vascular bed in humans.

Retinal blood vessels have been shown to change in diameter, branching angles or tortuosity, as a result of a disease, such as :Hypertensiondiabetes mellitus retinopathy of prematurity (ROP)

3

IntroductionGreen band is chosen in the present work because it is known

to show the improved visibility of the retinal blood vessels.

4

Multiscale

Retinal blood vessels have a range of different sizes.

Multiscale techniques have been developed to provide a way to isolate information about objects in an image by looking for geometric features at different scales.

5

MultiscaleThe effect of convolving an image with a Gaussian kernel

is to suppress most of the structures in the image with a characteristic length less than s.

6

Feature extractionGradient magnitude - The magnitude of the gradient

represents the slope of the image intensity for a particular value of the scale parameter s.

7

Feature extractionPrincipal curvature - Since vessels appear as ridge-like

structures in the images, we look for pixels where the intensity image has a local maximum in the direction for which the gradient of the image undergoes the largest change (largest concavity).

8

Feature extractionThe second derivative information is derived from the

Hessian of the intensity image I(x,y):

The eigenvalues, λ+and λ-, where we take λ+ ≥ λ-, measure convexity and concavity in the corresponding eigendirections.

9

Feature extraction

10

Feature extractionIn order to analyse both red-free and fluorescein images

with the same algorithm, we define

λ1 = min(|λ+|, |λ-|) and λ2 = max(|λ+|, |λ-|).

The maximum eigenvalue, λ2 , corresponds to the maximum principal curvature of the Hessian tensor, which we will refer to as maximum principal curvature.

11

Multiscale integrationThis might be expected, particularly for maximum

principal curvature, since the vessels are approximately cylindrical so that the total amount of blood in the light path corresponding to each pixel is larger in large vessels.

Vessels with diameter d ≈ 2s are most strongly detected when the scale factor is s, we normalised each feature along scales by d and then kept the local maxima over scales:

12

Multiscale integration

13

Region growingThe region growing algorithm we use is based on an

iterative relaxation technique, using:

Histograms of the extracted features, primarily upon the maximum principal curvature κ.

The classification of the eight-neighbouring pixels

14

Region growingIn first stage, classes grow initially in regions with low

gradient magnitude, γ, allowing a relatively broad and fast classification while suppressing classification in the edge regions where the gradients are large.

In second stage, the classification constraint is relaxed and classes grow based solely upon κ to allow the definition of borders between regions.

15

Region growing

16

Result

17

Comparison with average diameter

18

ConclusionsMS doesn’t perform better than other method. However,

we concluded that an analysis of true and false positive rate values does not give us the information needed about the accuracy of segmenting vessels when geometric measurements such as vessel widths are of interest.

And from the third validation, we conclude that the approach presented gives comparable estimates of diameter and branching angles in both red-free and fluorescein image.

19