A HPC Paradigm for identifying retinal vessels Carmen Alina Lupascu, Luigi di Rosa, Domenico Tegolo Dipartimento di Matematica e Applicazioni Università degli Studi di Palermo GRISU' Open Day su Bio-immagini e Grid, Wednesday 11 March 2009, Napoli, Italy

Automated detection of optic disc location in retinal images Vessel Segmentation? Vessel segmentation is a very important topic in various medical diagnoses, revealing the state of the disease.  Blood vessels can act as landmarks for image-guided laser treatment of choroidal neovascularization, hence reliable automatic detection of blood vessels is needed. GRISU' Open Day su Bio-immagini e Grid, Wednesday 11 March 2009, Napoli, Italy IEEE CBMS2008 June 17-19 2008, Jyväskylä, Finland

Retinal vessel diameter measurements may predict risk of coronary heart disease (CHD) and stroke deaths in middle-aged persons, but also early measurements in high-risk preterm infants may assess the development of severe retinopathy of prematurity (ROP). Retinal vessel tortuosity measurements may give information about disease severity or change of disease with time. Tortuosity of the blood vessel network can be produced by diseases like high blood flow, angiogenesis and blood vessel congestion.

Automated detection of optic disc location in retinal images Supervised method for vessel segmentation Pixel classification based on supervised methods require hand-labeled ground truth images for training. Sinthanayothin et al. (1999) classify pixels using a multilayer perceptron neural net, for which the inputs were derived from a principal component analysis (PCA) of the image and edge detection of the first component of PCA. Niemeijer et al. (2004) extract for each pixel from the green plane a simple feature vector and then use a K-nearest neighbor (kNN) that evince the probability of being a vessel pixel Soares et al. (2006), each image pixel is classified as vessel or non vessel based on the pixel's feature vector, which is composed of the pixel's intensity and two-dimensional Gabor wavelet transform. GRISU' Open Day su Bio-immagini e Grid, Wednesday 11 March 2009, Napoli, Italy IEEE CBMS2008 June 17-19 2008, Jyväskylä, Finland

Automated detection of optic disc location in retinal images Our Features Vector Features are extracted from the green plane of the retinal images, because in the green plane the contrast between vessel and background is higher than in the blue or red plane. The scales used in order that vessels with various dimensions could be detected were 4, hence the total number of features is 41. GRISU' Open Day su Bio-immagini e Grid, Wednesday 11 March 2009, Napoli, Italy IEEE CBMS2008 June 17-19 2008, Jyväskylä, Finland

Our Features Vector HPC-Area The feature vector consisted of: The Gaussian and its derivatives up to order 2. (6 features by the number of scales) A multiscale matched filter for vessels using a Gaussian vessel profile. (1 feature)

Our Features Vector HPC-Area Frangi’s vesselness measure . (2 features) Lindeberg’s ridge strength. (3 features) Staal’s ridges. (1 feature) Values of the principal curvatures. (2 features) Values of the mean curvature. (1 feature) Values of the principal directions. (4 features) Values of the gradient. (1 feature) The green channel intensity of each pixel. (1 feature)

Automated detection of optic disc location in retinal images AdaBoost classifier HPC-Area The training set within the DRIVE database consisted of 20 images of the dimension of 584 by 565 pixels, hence for training we had to choose from 6.599.200 pixels (but we considered only the pixels inside the FOV which are only 4.449.836). Due to the large number of pixels, only 789.914 pixels samples where randomly chosen to train the classifier, 789.914/20 pixels from each image. AdaBoost is an iterative algorithm for constructing a strong classifier as a linear combination of weak classifiers. The final strong classifier takes the form of a perceptron, a weighted combination of weak classifiers which is followed by a threshold. GRISU' Open Day su Bio-immagini e Grid, Wednesday 11 March 2009, Napoli, Italy IEEE CBMS2008 June 17-19 2008, Jyväskylä, Finland

Digital Retinal Images for Vessel Extraction Automated detection of optic disc location in retinal images DRIVE database Digital Retinal Images for Vessel Extraction http://www.isi.uu.nl/Research/Databases/DRIVE/ Database containing 20 training images and 20 test images. Each image is accompanied by its mask image and its manual segmentation. GRISU' Open Day su Bio-immagini e Grid, Wednesday 11 March 2009, Napoli, Italy 9 IEEE CBMS2008 June 17-19 2008, Jyväskylä, Finland 9

Experimental evaluation Automated detection of optic disc location in retinal images Experimental evaluation The performance are measured using Receiver Operating Characteristic (ROC) curves. ROC curves are represented by plotting True Positive Fractions (TPF) versus False Positive Fractions (FPF) as the discriminating threshold of the AdaBoost algorithm is varied. GRISU' Open Day su Bio-immagini e Grid, Wednesday 11 March 2009, Napoli, Italy IEEE CBMS2008 June 17-19 2008, Jyväskylä, Finland

The area under the ROC curve and the accuracy Automated detection of optic disc location in retinal images The area under the ROC curve and the accuracy The area under the ROC curve (Az) measures the ability of the classifier to correctly distinguish between vessel and non vessel pixels. An area of 1 indicates a perfect classification. The accuracy (ACC) is the fraction of pixels correctly classified and represents the degree of veracity of the classification. GRISU' Open Day su Bio-immagini e Grid, Wednesday 11 March 2009, Napoli, Italy IEEE CBMS2008 June 17-19 2008, Jyväskylä, Finland

AdaBoost classifier on DRIVE database Automated detection of optic disc location in retinal images AdaBoost classifier on DRIVE database B E S T R U L Our segmentation Ground truth segmentation ROC curve associated (ACC=0.9718) GRISU' Open Day su Bio-immagini e Grid, Wednesday 11 March 2009, Napoli, Italy IEEE CBMS2008 June 17-19 2008, Jyväskylä, Finland

AdaBoost classifier on DRIVE database Automated detection of optic disc location in retinal images AdaBoost classifier on DRIVE database W O R S T E U L Our segmentation Ground truth segmentation ROC curve associated (ACC=0.9441) GRISU' Open Day su Bio-immagini e Grid, Wednesday 11 March 2009, Napoli, Italy IEEE CBMS2008 June 17-19 2008, Jyväskylä, Finland

Automated detection of optic disc location in retinal images AdaBoost classifier on DRIVE database After each of the 20 test images from the DRIVE database has been segmented using AdaBoost classifier, the ROC curves are constructed. The mean of the areas under these ROC curves is 0.9560 and the mean of the accuracies is 0.9584. GRISU' Open Day su Bio-immagini e Grid, Wednesday 11 March 2009, Napoli, Italy IEEE CBMS2008 June 17-19 2008, Jyväskylä, Finland

Automated detection of optic disc location in retinal images Overview of the performance of different methods on DRIVE database Segmentation method DRIVE database Az ACC Our method 0.9560 0.9584 Soares et al. 0.9614 0.9466 Human observer - 0.9473 Staal et al. 0.9520 0.9442 Niemeijer et al. 0.9294 0.9416 Zana et al. 0.8984 0.9377 Jiang et al. 0.9114 0.9212 Martinez et al. 0.9181 Chaudhuri et al. 0.7878 0.8773 GRISU' Open Day su Bio-immagini e Grid, Wednesday 11 March 2009, Napoli, Italy IEEE CBMS2008 June 17-19 2008, Jyväskylä, Finland

Automated detection of optic disc location in retinal images Computational time HARDWARE: the method was tested on an Intel(R) Core Duo CPU (3.16 GHz) with 4GB memory. FEATURE: feature generation for an image from the DRIVE database takes less than 2 minutes. CLASSIFICATION: the classification of its pixels takes less than 5 seconds. THE MODEL: The process of learning the AdaBoost model is computationally more expensive. It takes almost 4 hours. EXPENSIVE!!!! GRISU' Open Day su Bio-immagini e Grid, Wednesday 11 March 2009, Napoli, Italy IEEE CBMS2008 June 17-19 2008, Jyväskylä, Finland

Single Neuron Properties our other activities Optical Disk Discovery Islets of Langherans 3D Rendering Pancreas Single Neuron Properties A Methodology for Classifying Megakaryocytes 3D Rendering Spleen GRISU' Open Day su Bio-immagini e Grid, Wednesday 11 March 2009, Napoli, Italy

Bibliography B.Ballarò, A.M.Florena, V.Franco, D.Tegolo, C.Tripodo, C.Valenti, “An automated image analysis methodology for classifying megakaryocytes in chronic myeloproliferative disorders”, Medical Image Analysis, Volume 12, Issue 6, December 2008, Pages 703-712 M.Migliore, G.Novara, D.Tegolo, “Single neuron binding properties and the magical number 7”, Hippocampus, Vol. 18, Issue 11, November 2008, Pages 1122-30 C.Tripodo, C.Valenti, B.Ballarò, Z.Rudzki, D.Tegolo, V.Di Gesù, A.M.Florena, V.Franco, “Megakaryocytic features useful for the diagnosis of myeloproliferativedisorders can be obtained by a novel unsupervised software analysis”, Histol Histopathol, Volume 21, number 8 (August), 2006 C.A.Lupascu, D.Tegolo, L.Di Rosa, “Automated Detection of Optic Disc Location in Retinal Images”, 21st IEEE International Symposium on Computer-Based Medical Systems, 2008. CBMS '08, 17-19 June 2008 Pages:17-22. C.Grimaudo, D.Tegolo, C.Valenti, F.Bertuzzi, “Image Segmentation to Evaluate Islets of Langherans”, Proceedings of the First International Conference on Biomedical, Electronics and Devices, BIOSIGNALS 2008, Funchal, Madeira, Portugal. January 28-31, 2008, Volume 1, ISBN 978-989-8111-18-0, Pages: 72-76 GRISU' Open Day su Bio-immagini e Grid, Wednesday 11 March 2009, Napoli, Italy