Pixel Parallel Vessel Tree Extraction for a Personal Authentication System

2010/01/14學生：羅國育

Outline Introduction Vessel Pattern-based Authentication System Pixel Parallel Vessel Tree Extraction Algorithm Authentication System Experimental Results Conclusion

Introduction In this paper, a pixel parallel approach is proposed

to tackle with the retinal vessel tree extraction to be used in a personal retinal authentication system, regarding the computation speed.

Vessel Pattern-based Authentication System (1/2)

In this system, the landmarks (bifurcations) of the retinal vessel tree are extracted to characterize the individual.

The use of this biometric feature points. Once the images are aligned, a similarity measure

is computed, and if it is higher than a given threshold the individual is accepted, otherwise he is rejected.

Vessel Pattern-based Authentication System (2/2)

Pixel Parallel Vessel Tree Extraction Algorithm (1/2)

Due to the high resolution of the retinal images, the original image is split into sub windows in order to fit the size of the current chip implementations without losing image resolution information.

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Pixel Parallel Vessel Tree Extraction Algorithm (2/2)

Fig. 2. Flow diagram with the building blocks of the proposed algorithm:Stage 1: Vessel region pre-estimation, Stage 2: Initial region estimation, Stage

3: External potential estimation, Stage 4: PLS evolution

Authentication System (1/4) Point feature extraction: Four steps have been pro

posed to implement the point feature extraction. When a non-zero pixel is detected, its non-zero nei

ghbours are tracked recursively following the line directions.

Authentication System (2/4) Registration and matching: A matching value is

computed to obtain the similarity between both images, which will determine the acceptance or rejection of the individual.

Authentication System (3/4) The similarity value, S, for a pair of points is

computed based on the distance between them:

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(1)

(2)

Authentication System (4/4) A matrix, Q, is computed, such that the

position (i, j) holds the value P(A,B) for all the combinations.

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,1 (3)

Experimental Results (1/4) The images used in the experiment have been acqu

ired during a period of 15 months in the University Hospital of Santiago de Compostela (CHUS), using a Cannon CR6-45NM Non-Mydriatic Retinal Camera, with a resolution of 768x584 pixels.

Experimental Results (2/4) The original retinal image has been split into 128x

128 sub-windows to fit the current size of the parallel processor arrays.

The execution time required in this chip to perform the whole algorithm for a 128x128 subwindow is 6.5 ms.

Since the image resolution is 768x584, a total number of 30 sub-windows is required. So, the global execution time required to process the whole retinal vessel tree (excluding I/O operations) is 0.1925 s.

Experimental Results (3/4) A set of 100 images were introduced to the system

(12 of them belonging to 4 different individuals, acquired in different times).

The mean execution time for the authentication stages (excluding the retinal vessel tree extraction) is about 250ms.

The whole authentication process is about 0.44 s.

Fig. 3. Left: Retinal vessel tree (previous to the skeletonization) over theoriginal image, Right: skeleton with feature points used to authenticate

Experimental Results (4/4) Setting the threshold in values around 0.3 and 0.4,

the effectiveness of the system using this database is 100% .

Conclusion Although the effectiveness of the system is 100%

using the proposed set, more tests should be done to determine an accurate threshold value.

112/05/14

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