vessels delineation in retinal images using cosfire...
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Vessels delineation in retinal images using COSFIRE filters
George Azzopardi1,3, Nicola Strisciuglio1,2, Mario Vento2, Nicolai Petkov1
1University of Groningen (The Netherlands) - 2University of Salerno (Italy) – 3University of Malta
university of salerno
BICV Summer School, Shenyang, China, 2015 1
Delineation
Automated delineation of elongated structures gained great interest in the image processing community Applications: detection of crack in walls, segmentation of rivers in aerial images, extraction of blood vessels from biomedical images
BICV Summer School, Shenyang, China, 2015 2
Motivation
BICV Summer School, Shenyang, China, 2015
› The structure of the retinal vascular tree can reveal signs of cardiovascular diseases
for vessel delineation can speed-up the › An automatic process diagnosis process
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Related works
BICV Summer School, Shenyang, China, 2015
• 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
› Unsupervised Methods
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Filter Configuration
BICV Summer School, Shenyang, China, 2015
› The CORF/COSFIRE filter is trainable
Prototype pattern DoG Response Local intensity maxima
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Filter Configuration
BICV Summer School, Shenyang, China, 2015
› The CORF/COSFIRE filter is trainable
Local intensity maxima
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Filter Model
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Pipeline
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Rotation Invariance
45° 60° 75°
BICV Summer School, Shenyang, China, 2015
90° 105°
0° 15° 30°
120° 135° 150° 165°
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Rotation Invariance
45° 60° 75°
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Data sets
BICV Summer School, Shenyang, China, 2015
› 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
DRIVE STARE CHASE_DB1
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Configured Filters
BICV Summer School, Shenyang, China, 2015
› A bar-selective (symmetric) COSFIRE filter is configured to detect vessels
Original image Bar selective
(12 orientations)
Filter output
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Configured Filters
BICV Summer School, Shenyang, China, 2015
› A bar-selective (symmetric) COSFIRE filter is configured to detect vessels
Original image Ground truth Filter output
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Configured Filters
BICV Summer School, Shenyang, China, 2015
› A bar-ending-selective (asymmetric) COSFIRE filter is configured to be responsive on vessel-endings
Ground truth Asymmetric filter output
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Configured Filters
BICV Summer School, Shenyang, China, 2015
› A bar-ending-selective (asymmetric) COSFIRE filter is configured to be responsive on vessel-endings
Ground truth Symmetric filter output Asymmetric filter output
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Performance Evaluation
BICV Summer School, Shenyang, China, 2015
› 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
BICV Summer School, Shenyang, China, 2015
› A r e a u n d e r R O C curve
› DRIVE = 0.9614 › STARE = 0.9563 › CHASE_DB1= 0.9487
Close to Human observer performance (no statistical difference)
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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
BICV Summer School, Shenyang, China, 2015
› Most efficient method for vessel delineation in retinal images ever published
*Processing time is reported for DRIVE and STARE data sets
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Robustness to noise
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Supervised method
• Delineation of vessels of different thickness • Selection of the most relevant filters for the application at hand by
Generalized Matrix Learning Vector Quantization (GMLVQ)
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COSFIRE filter-bank Pixel-wise features Filters selection Classification
Relevances (GMLVQ) SVM
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A bank of B-COSFIRE filters
• We configure a bank of 21 vessels detector and 21 vessel-endings detector (deal with vessels of different thickness)
• We describe each pixel with a vector of the responses of the configured filters
Vessel selective (12 orientations)
Vessel-ending selective (24 orientations)
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A bank of B-COSFIRE filters
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Filter selection
• GMLVQ evaluates the relevance of the single and all of pairs of filters (matrix of relevance)
• We select the filters with local relevance maxima
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Classification
• We use the responses of the selected filters to form pixel-wise feature vectors to describe vessel and non-vessel pixels
• In order to account for skewness in the data we perform the Inverse
Hyperbolic Sine Transformation: • We train a SVM classifier to distinguish between vessel and non-vessels
pixels
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Results
B-COSFIRE B-COSFIRE
› We compute the final results for a given threshold, the one that provides the maximum average MCC on a given data set
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Results Comparison
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Automa'c differen'a'on of u-‐ and n-‐serrated pa4erns in DIF images
Chenyu Shi, Jiapan Guo, George Azzopardi, Nicolai Petkov
BICV Summer School, Shenyang, China, 2015 29
Background
• A type of skin disease: pemphigoid • A special pemphigoid: Epidermolysis bullosa acquisita (EBA) • EBA : an autoimmune blistering disease and shares similar clinical
features with other types
Reference: Buijsrogge, J.J.A., Diercks, G.F.H., Pas, H.H., Jonkman, M.F.: The many faces of epidermolysis bullosa acquisita aFer serraGon paHern analysis by direct immunofluorescence microscopy. BriGsh Journal of Dermatology 165(1), 92–98 (JUL2011)
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Serration pattern analysis
EBA à u-serrated pattern à finger-like shapes Others à n-serrated pattern à undulating n-shapes
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So far there are no automatic techniques to distinguish between these two types of serration patterns.
u-‐serrated pa4erns n-‐serrated pa4erns
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Method
Step 1: Segmentation of the region of interest Step 2: Detect ridges and determine the orientations of each location Step 3: Use normalized histogram of orientations as the feature vector
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Segmentation of the region of interest
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Examples of DIF images
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Detect ridges with CORF model
Rota'on-‐tolerant result
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Determine orienta'on of the boundary
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Use normalized histogram of orientations as the feature vector
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Experiments
Data set (Medical Hospital in Groningen) • 416 DIF images
• 240 n-‐serrated • 176 u-‐serrated
Results
• RecogniTon rate of 84.6% on the UMCG public data set of 26 images, which is comparable to the performance of medical experts 82.1%.
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Conclusions
• Highly effective and robust approach for vessel segmentation in retinal images and ridge detection
• We proposed a general framework for the delineation of elongated patterns: the features are domain-independent
• The B-COSFIRE filter is versatile as it can be configured to detect any pattern of interest
• Very efficient
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Outlook
BICV Summer School, Shenyang, China, 2015
• Vessel delineation with push-pull inhibition?
• Use bifurcation- and crossover-selective filters
• Delineation of 3D vessels in angiography images of the brain by adding depth information to the model
• Parallelization of the algorithm, which can run on GPUs
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References
BICV Summer School, Shenyang, China, 2015
› [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
BICV Summer School, Shenyang, China, 2015
› [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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