Vessels delineation in retinalimages using COSFIRE filters

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1University of Groningen (The Netherlands) - 2University of Salerno (Italy)

George Azzopardi1, Nicola Strisciuglio1,2, Mario Vento2, Nicolai Petkov1

!university of salerno

Full paper: “Trainable COSFIRE filters for vessel delineation with application to retinal images”, Medical Image Analysis, Available Online 3 September 2014, DOI: 10.1016/j.media.2014.08.002

Motivation

› The structure of the retinal vascular tree can reveal signs of cardiovascular diseases

› An automatic process for vessel delineation can speed-up the diagnosis process

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Related works

› Unsupervised Methods

• Mainly based on convolution and matched filters [3-4], mathematical morphology [5]

• Suffer from high sensitivity to noise

› Supervised Methods

• Based on pixel-wise feature vectors computation and classification with machine learning tools [6-9]

• High computational time

• Complex learning procedures

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COSFIRE

› COSFIRE: Combination Of Shifted FIlter REsponses› Filtering approach based on the response of some cells in the visual

cortex› Already demonstrated to be effective for contour detection [1],

keypoint/objects detection and feature description [2]

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Contour detection Traffic sign recognition in complex scenes

Handwritten digits recognitionMNIST: 99.48%

Filter Configuration

› The COSFIRE filter is trainable

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Filter Configuration

› The COSFIRE filter is trainable

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Prototype pattern

Filter Configuration

› The COSFIRE filter is trainable

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Prototype pattern DoG Response

Filter Configuration

› The COSFIRE filter is trainable

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Prototype pattern DoG Response Local intensity maxima

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Filter Configuration

› The COSFIRE filter is trainable

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Local intensity maxima

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Filter Model

Pipeline

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Pipeline

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Pipeline

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Pipeline

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Rotation Invariance

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0°

90°

15°

105°

30°

120°

45°

135°

60°

150°

75°

165°

Rotation Invariance

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Data sets

› DRIVE: 40 JPEG images at 768x584 pixels (20 training, 20 testing) › STARE: 20 JPEG images at 700x605 pixels › CHASE_DB1: 28 JPEG images at 1280x960 pixels

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DRIVE STARE CHASE_DB1

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Bar selective(12 orientations)

Configured Filters

› A bar-selective (symmetric) COSFIRE filter is configured to detect vessels

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Configured Filters

› A bar-selective (symmetric) COSFIRE filter is configured to detect vessels

Original image Ground truth Filter output

Configured Filters

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› A bar-ending-selective (asymmetric) COSFIRE filter is configured to be responsive on vessel-endings

Bar-ending selective(24 orientations)

Configured Filters

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› A bar-ending-selective (asymmetric) COSFIRE filter is configured to be responsive on vessel-endings

Ground truth Symmetric filter output Asymmetric filteroutput

Performance Evaluation

› We measured the performance in terms of:

• Matthews Correlation Coefficient (MCC)

• Sensitivity (Se)

• Specificity (Sp)

• Accuracy (Acc)

• Area under ROC curve (AUC)

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ROC curves

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Area under ROC curveDRIVE = 0.9614STARE = 0.9563CHASE_DB1= 0.9487

ROC curves

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Area under ROC curveDRIVE = 0.9614STARE = 0.9563CHASE_DB1= 0.9487Close to Human

observer performance(no statistical difference)

Results Comparison (1/3)

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Results Comparison (2/3)

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Results Comparison (3/3)

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Time Efficiency

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› Most efficient method for vessel delineation in retinal images ever published

*Processing time is reported for DRIVE and STARE data sets

Robustness to noise

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Conclusions

› Highly effective approach for vessel delineation in retinal images

› Most efficient method ever published in literature for vessel delineation in retinal images

› Robust to the noise and deformations of the pattern of interest

› The COSFIRE filters is versatile as it can be configured to detect any pattern of interest

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Future Works

› Exploring the scale invariance by constructing a pixel-wise feature vector with the response of the filter at different scales

› Delineation of 3D vessels in angiography images of the brain by adding depth information to the model

› Parallelization of the algorithm

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References

› [1] A CORF computational model of a simple cell that relies on LGN input outperforms the Gabor function model. Biological Cybernetics 106, 177-189.

› [2] Trainable COSFIRE filters for keypoint detection and pattern recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 35, 490-503.

› [3] Al-Rawi, M., Qutaishat, M., Arrar, M., 2007. An improved matched filter for blood vessel detection of digital retinal images. Computer in biology and medicine 37, 262-267.

› [4] Hoover, A., Kouznetsova, V., Goldbaum, M., 2000. Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Transactions on medical imaging 19, 203-210.

› [5] Mendonca, A.M., Campilho, A., 2006. Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction. IEEE Transactions on Medical Imaging 25, 1200-1213.

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References

› [6] Ricci, E., Perfetti, R., 2007. Retinal blood vessel segmentation using line operators and support vector classification. IEEE Transactions on medical imaging 26, 1357-1365.

› [7] Staal, J., Abramo, M., Niemeijer, M., Viergever, M., van Ginneken, B., 2004. Ridge-based vessel segmentation in color images of the retina. IEEE Transactions on medical imaging 23, 501-509.

› [8] Marin, D., Aquino, A., Emilio Gegundez-Arias, M., Manuel Bravo, J., 2011. A New Supervised Method for Blood Vessel Segmentation in Retinal Images by Using Gray-Level and Moment Invariants-Based Features. IEEE Transactions on medical imaging 30, 146-158.

› [9] Soares, J.V.B., Leandro, J.J.G., Cesar, Jr., R.M., Jelinek, H.F., Cree, M.J., 2006. Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification. IEEE Transactions on medical imaging 25, 1214-1222.

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