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Ridge-Based

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    Slide 1:Ridge-Based Vessel Segmentation in Color images of the Retina Staal et al, IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 23, NO. 4, APRIL 2004

    Final Presentation Amir Tamrakar May 19, 2005

    Slide 2:Overview

    Goal + Clinical Relevance Review of Methodology Brief Recap of the Feature Computation Stage Training a classifier on the features Applying the classifier on test data Present Results Discuss limitations + Possible Improvements

    Slide 3:Goal

    Auto-segment vessels in color retinal images for the purpose of screening for diabetic retinopathy. 5.5% of the population worldwide are diabetic 10% of all diabetic patients develop diabetic retinopathy Diabetic retinopathy is the primary cause of blindness in the west. It can be prevented with treatment at early stages. Hence the need for screening.

    Slide 4:Related Work/ Other Methods for Vessel Extraction

    Matched Filters Grouping edge pixels Locally adaptive thresholding Topology Adaptive Snakes Vessel tracking Matched Kalman Filters Fuzzy clustering and tracking Morphology based Techniques (forming trees) Supervised learning methods Neural Networks References for these works can be found in the Staal etal paper.

    Slide 5:Methodology

    Retinal image (Green channel only) Extract ridges Form line element primitives Form patches around primitives Vessels segmented Classifiers (vessel / no vessel) Extract Features

    Slide 6:Overall Insight

    GOAL: To Learn to Classify every pixel (independently) as vessel or non-vessel within the FOV. A pixel representation is not optimal for vessel structure. Knowing that vessels are elongated structures, one can represent vessels with elongated vessel primitives. Height ridges coincide approximately with vessel centerlines so form these primitives along ridge curves (convex sets of connected ridge points)

    Slide 7:Overall Insight contd..

    Divide the Classification Task into 2 parts: Train Classifier #1 to compute the probability that a given Convex set (CS) is part of a vessel using local features around the primitive. Divide the image into patches around the CSs (i.e., assign each pixel in the image to its nearest primitive.) Within each patch, a local coordinate system can be defined and local features can be extracted for every pixel. Train Classifier #2 to classify every pixel using the local patch features into vessel or non-vessel. The output of Classifier #1 is one of the features for Classifier #2 (Needless to say, the most important)

    Slide 8:INPUT: Color Image of the Retina

    Slide 9:1. Split into RGB Components

    Slide 10:2. Keep the Green Channel only

    Slide 11:3. Compute Ridge Points

    Slide 12:Details: Computing Ridge points

    R(x) = -1 : ridges, +1 : valleys Numerically: v1 is the eigenvector corresponding to the largest eigenvalue

    Slide 13:4. Link ridge points into convex sets (CS)

    Slide 14:Details: Linking Ridge points

    r1 r2 vug1 vug2 vg ec Algorithm: Region growing from random seeds Rules: Tg

    Original implementation: (region growing using spiral search) New implementation: Curve growing (ep = 0.95) ep = 0.8 But curves were still doubling back. Added rule:

    Slide 17:5. Form patches around the convex sets (CSR)

    Slide 18:Details: Forming Convex set regions

    Compute Chamfer Distance Transform from ridge points in convex sets Propagate distance + set label + closest point information Distance Transform

    Slide 19:Details: Forming Convex set regions

    Compute Chamfer Distance Transform from ridge points in convex sets Propagate distance + set label + closest point information Closest Point Map

    Slide 20:Details: Forming Convex set regions

    Compute Chamfer Distance Transform from ridge points in convex sets Propagate distance + set label + closest point information Labeled CSR

    Slide 21:Details: Convex set features (at ridge points)

    18 features 8 are extracted from average profile (?)(-15..15) Height: h = ?(0) Width: w = (nre-nle) h/w Edge strength: se = ?(nle) + ?(nre) se/w Edge height: he = (?(nle) + ?(nre))/2 h-he h/he 3 from convex set curves Distance between first and last point (d) The length of the curve (l) The curvature of the curve (k) Rest from image features Mean internsity (g) Standard deviation (sg) ?g/ /r Ridge Strength at s =0.5 ( average ?) Ridge Strength at s =1 ( average ?) Ridge Strength at s =2 ( average ?) Ridge Strength at s =3 ( average ?)

    Slide 22:6. Compute several features from the convex sets (18)

    Slide 23:Details: Convex Set Region features (at every pixel)

    r(x) g(x) g(x)/r(x) p(c=vessel) dcl = || x- xcl || r(x) r(xcl) r(x)/r(cl) g(x)-g(xcl) g(x)/g(xcl) x y Ix (at s =0.5, 1, 2, 4 ) Iy (at s =0.5, 1, 2, 4 ) Ixx (at s =0.5, 1, 2, 4 ) Iyy (at s =0.5, 1, 2, 4) Fit a line through the CS points & Define a local coordinate system (x, y)

    Slide 24:7. Compute some more features from the CSRs (27)

    Slide 25:8. Train a classifier of some kind

    Supervised Learning Algorithm Feature set Ground Truth 17 CS features 27 CSR features

    Slide 26:8. (Revised) Train a couple of classifiers [of some kind]

    Train Classifier #2 Ground Truth (Hand segmented Images) 17 CS features 27 CSR features Train Classifier #1 Learned Parameters

    Slide 27:9. Applying the Classifiers on Test data

    Classifier #2 17 CS features 27 CSR features Classifier #1 Test Image Segmented image

    Slide 28:Various Statistics

    Database: Utrecht DRIVE database (http://www.isi.uu.nl/Research/Databases/DRIVE/) 40 images + Manual segmentations 20 images used to train and 20 to test Pathologies Training set: 3 images Test set: 4 images Image: 584x565 JPEG color images # of pixels: 329960 FOV mask: Circular (diameter = 460 pixels) # of pixels within the FOV: 209010 # of pixels marked as Vessel (GT): 24265 (11.6%)

    Slide 29:Classifier

    Authors used k-NN classifier (k=101) I used AdaBoost instead Data volume was easier to manage Feature selection not required Fast inference process

    Slide 30:A weak learner

    weak learner A weak rule h h

    Slide 31:The boosting process

    Final rule: Sign[ ] h1 a1 + h2 a2 + hT aT +

    Slide 32:Adaboost (Adaptive Boosting)

    Binary labels y = -1,+1 margin(x,y) = y [St at ht(x)] P(x,y) = (1/Z) exp (-margin(x,y)) Given ht, we choose at to minimize S(x,y) exp (-margin(x,y))

    Slide 33:Discriminative ability of the CS features

    Slide 34:Discriminative ability of the CSR features

    Slide 35:Results

    Original Image Vessels segmented Accuracy: 94.4% wrt Manual Segmentation

    Automatic Segmentation Manual Segmentation

    Original Image Vessels segmented

    Accuracy: 96% wrt Manual Segmentation Original Image Vessels segmented

    Slide 39:Images with Pathologies

    Original Image Vessels segmented Accuracy: 92.4% wrt Manual Segmentation

    Slide 40:Images with Pathologies

    Original Image Vessels segmented Accuracy: 92.5% wrt Manual Segmentation

    Slide 41:Results Summary

    Overall Accuracy = 93.9% Best segmentation = 96% Worst segmentation = 92.4%

    Slide 42:Limitations

    Needs a lot of Training data (Tremendous: manual effort). Especially images with pathologies if it is to correctly segment pathologies (Aneurysms) Pathologies are not recognized and sometimes misclassified as vessels Over and under segmentation (a concern if objective vessel width measurements are required) A lot of small vessels are missed Some vessels appear thickened Edges of the optical disc often have vessel like features and so are segmented as vessels.

    Slide 43:Future Work

    Some of the errors are due to randomization during ridge point linking process Arbitrarily partitions the ridge curves into convex sets. More rigorous partitioning will avoid small patches which get thrown out and create gaps in the segmentation Use other classifiers e.g., k-NN classifiers with Feature selection in the AdaBoost Framework. Add features?

    Slide 44:References

    Staal et al, Ridge-Based Vessel Segmentation in Color images of the Retina, IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 23, NO. 4, APRIL 2004