Vessels delineation in retinal images using COSFIRE filters

Transcription

1 university of salerno 1 Vessels delineation in retinal images using COSFIRE filters George Azzopardi 1,3, Nicola Strisciuglio 1,2, Mario Vento 2, Nicolai Petkov 1 1 University of Groningen (The Netherlands) - 2 University of Salerno (Italy) 3 University of Malta BICV Summer School, Shenyang, China,

2 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,

3 Motivation The structure of the retinal vascular tree can reveal signs of cardiovascular diseases An automatic process diagnosis process for vessel delineation can speed-up the BICV Summer School, Shenyang, China,

4 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 BICV Summer School, Shenyang, China,

5 Filter Configuration The CORF/COSFIRE filter is trainable Prototype pattern DoG Response Local intensity maxima BICV Summer School, Shenyang, China,

6 Filter Configuration The CORF/COSFIRE filter is trainable Filter Model Local intensity maxima BICV Summer School, Shenyang, China,

7 Pipeline BICV Summer School, Shenyang, China,

8 Rotation Invariance BICV Summer School, Shenyang, China,

9 Rotation Invariance BICV Summer School, Shenyang, China,

10 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 DRIVE STARE CHASE_DB1 BICV Summer School, Shenyang, China,

11 Configured Filters A bar-selective (symmetric) COSFIRE filter is configured to detect vessels Original image Bar selective (12 orientations) BICV Summer School, Shenyang, China, 2015 Filter output 11

12 Configured Filters A bar-selective (symmetric) COSFIRE filter is configured to detect vessels Original image Ground truth Filter output BICV Summer School, Shenyang, China,

13 Configured Filters A bar-ending-selective (asymmetric) COSFIRE filter is configured to be responsive on vessel-endings Ground truth BICV Summer School, Shenyang, China, 2015 Asymmetric filter output 13

14 Configured Filters A bar-ending-selective (asymmetric) COSFIRE filter is configured to be responsive on vessel-endings Ground truth Symmetric filter output Asymmetric filter output BICV Summer School, Shenyang, China,

15 Performance Evaluation We measured the performance in terms of: Matthews Correlation Coefficient (MCC) Sensitivity (Se) Specificity (Sp) Accuracy (Acc) Area under ROC curve (AUC) BICV Summer School, Shenyang, China,

16 ROC curves Close to Human observer performance (no statistical difference) Are a u nde r R O C curve DRIVE = STARE = CHASE_DB1= BICV Summer School, Shenyang, China,

17 Results Comparison (1/3) BICV Summer School, Shenyang, China,

18 Results Comparison (2/3) BICV Summer School, Shenyang, China,

19 Results Comparison (3/3) BICV Summer School, Shenyang, China,

20 Time Efficiency Most efficient method for vessel delineation in retinal images ever published *Processing time is reported for DRIVE and STARE data sets BICV Summer School, Shenyang, China,

21 Robustness to noise BICV Summer School, Shenyang, China,

22 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) Relevances (GMLVQ) SVM COSFIRE filter-bank Pixel-wise features Filters selection Classification BICV Summer School, Shenyang, China,

23 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) BICV Summer School, Shenyang, China,

24 A bank of B-COSFIRE filters BICV Summer School, Shenyang, China,

25 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 BICV Summer School, Shenyang, China,

26 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 BICV Summer School, Shenyang, China,

27 Results We compute the final results for a given threshold, the one that provides the maximum average MCC on a given data set B-COSFIRE B-COSFIRE BICV Summer School, Shenyang, China,

28 Results Comparison BICV Summer School, Shenyang, China,

29 1 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,

30 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), (JUL2011) BICV Summer School, Shenyang, China,

31 Serration pattern analysis EBA à u-serrated pattern à finger-like shapes Others à n-serrated pattern à undulating n-shapes BICV Summer School, Shenyang, China,

32 So far there are no automatic techniques to distinguish between these two types of serration patterns. u- serrated pa4erns n- serrated pa4erns BICV Summer School, Shenyang, China,

33 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 BICV Summer School, Shenyang, China,

34 Segmentation of the region of interest BICV Summer School, Shenyang, China,

35 Examples of DIF images BICV Summer School, Shenyang, China,

36 Detect ridges with CORF model Rota'on- tolerant result BICV Summer School, Shenyang, China,

37 Determine orienta'on of the boundary BICV Summer School, Shenyang, China,

38 Use normalized histogram of orientations as the feature vector BICV Summer School, Shenyang, China,

39 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%. BICV Summer School, Shenyang, China,

40 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 BICV Summer School, Shenyang, China,

41 Outlook 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 Anyone interested for an internship in Malta? BICV Summer School, Shenyang, China,

42 References [1] A CORF computational model of a simple cell that relies on LGN input outperforms the Gabor function model. Biological Cybernetics 106, [2] Trainable COSFIRE filters for keypoint detection and pattern recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 35, [3] Al-Rawi, M., Qutaishat, M., Arrar, M., An improved matched filter for blood vessel detection of digital retinal images. Computer in biology and medicine 37, [4] Hoover, A., Kouznetsova, V., Goldbaum, M., Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Transactions on medical imaging 19, [5] Mendonca, A.M., Campilho, A., Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction. IEEE Transactions on Medical Imaging 25, BICV Summer School, Shenyang, China,

43 References [6] Ricci, E., Perfetti, R., Retinal blood vessel segmentation using line operators and support vector classification. IEEE Transactions on medical imaging 26, [7] Staal, J., Abramo, M., Niemeijer, M., Viergever, M., van Ginneken, B., Ridge-based vessel segmentation in color images of the retina. IEEE Transactions on medical imaging 23, [8] Marin, D., Aquino, A., Emilio Gegundez-Arias, M., Manuel Bravo, J., 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, [9] Soares, J.V.B., Leandro, J.J.G., Cesar, Jr., R.M., Jelinek, H.F., Cree, M.J., Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification. IEEE Transactions on medical imaging 25, BICV Summer School, Shenyang, China,