2_2008-Retinal Blood Vessel Segmentation Using Line Operators and Support Vector Classification

7/30/2019 2_2008-Retinal Blood Vessel Segmentation Using Line Operators and Support Vector Classification

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Retinal Blood Vessel Segmentation Using Line

Operators and Support Vector Classification

Source: IEEE Transactions on medical imaging, Vol. 26, No. 10, pp. 1357-1365, 2007

Authors: Elisa Ricci and Renzo Perfetti

Reporter: Pei-Yen Pai

Date: 2008.01.10


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Outline

Introduction

Proposed method

Experimental results

Conclusions


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Introduction (1/2)

Retinal image Vessel segmentation image


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Introduction (2/2)

Color retinal image Green channel of retinal

image


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Line Detector (1/4)

jiNjiLjiS ,,, S(i,j) is line strength of the pixel.L(i,j) is line with the largest average grey level.N(i,j) is the average grey level in the square

windows.

0

15


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Line Detector (2/4)


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Line Detector (3/4)


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Line Detector (4/4)

0000

0000

513is60and45,30fordirectionorthogonal:ex

135and,90,45,0:nsorientatio

,,, jiNjiLjiSoo

So(i,j) is line strength of the pixel three pixels.Lo(i,j) is line with the largest average greylevel.

N(i,j) is the average grey level in the squarewindows.

Assuming three pixels is considerer:


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Feature Vector

jiIjiSjiSX o ,,,

vector.featuretheof

deviationstandardandmeaningcorrespondtheareand,

321featureththeiswhere

tion)(Normaliza

ii

i

i

iii

,,iix

xx

I(i,j) is the gray level value at the pixel.


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SVM Classification


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Experiments

STARE database 20 images 700 x 605 pixels

FOV (field of view): 650 x 550 pixels

Manually segmented by 2 observers

First observer as ground truth

DRIVE database 40 images

768 x 584 pixels

FOV: 540 x 540 pixels

20 images for training set and 20 images for test set Four pathological images in the test set

Manually segmented by 2 observers on test set

First observer as ground truth

FOV


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STARE database

DRIVE database


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Experimental Results 1/6

SVM classifier: 20000 manually segmented pixels

from 20 images

SVM classifier: 20000 manually segmented pixels

from 20 training set images

TPR: The true positive rate.

FPR: The false positive rate.

TPR: 0.903

FPR: 0.061 TPR:0.775

FPR: 0.0275

(Receiver operating characteristic)


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AUC: the area under the ROC curve

ACCURACY: the total NO. of correctly classified pixels / the NO. of pixelsin the image FOV.


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Image segmented using the line detector


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Linear SVM Observer A Observer B

Linear SVM Observer A Observer B


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Conclusions

Simple computation

Good results with respect to existing unsupervised

methods

The supervised approach requires fewer feature thanexisting methods

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2_2008-Retinal Blood Vessel Segmentation Using Line Operators and Support Vector Classification