Active Contours Technique in Retinal Image Identification of the Optic Disk Boundary

Soufyane El-Allali Stephen Brown

Department of Computer Science and EngineeringUniversity of South CarolinaDr. Song Wang CSCE 790

Spring 2003

The Problem

Objective: Using active contours to find the optic disk boundary.

Impediments: Large image size Location of optic disk Noise Initialization

Solution Model

Image Pre-processing Significance: Without pre-

processing the active contours is strongly influenced by noise.

Phases: Thresholding Windowing Morphological techniques

Dilation Erosion Reconstruction

Thresholding & Windowing Threshold: Optic disk

corresponds to the brightest region.

Gradient marker level is set to obtain the threshold.

Optic disk region corresponds to 245-255 of the intensity level.

Windowing: cropped image based on the threshold.

Dilation Definition: Dilation causes objects to dilate or grow in size by

adding pixels to the boundaries of object in an image. Dilation depends on a structure element. Dilation algorithm.

Erosion and Reconstruction Definitions:

Erosion causes objects to shrink by removing pixels on object boundaries.

Reconstruction takes the maximum pixel value from the original image and the dilated/eroded image.

Active Contours Revisted Definition: Active contours (snakes) is an edge-based

technique that defines curves within an image domain that can move under the influence of internal and external forces in order to achieve convergence along an object.

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Gaussian function’s standarddeviation

Active Contours Continued Objective: minimizing the energy functional Solution: must satisfy Euler Lagrange’s Equation

Bringing the snakes to equalibrium: Adding a damping term and an inertial term

Simple solution: using the gradient descent algorithm

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Gradient Vector Flow (GVF) Traditional snakes: has a tendency not to converge in the case of

concave shapes. GVF: Proposed by Xu and Prince

Static external force h = (p, q) Minimizes the energy function

Solution: solving the Euler system

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Experiment Traditional snakes Before & After

Preprocessing Initialization Superposition of

GVF fields Results

Initialization Incorrect initialization

leads to inaccurate results.

Example: Snake initialized in an

empty GVF field. Results in snake resting

in same area.

Before & After Pre-processing

GVF fields Superposition Motive:

Larger Gaussian standard deviation captures the object of interests, yet blurring the edge boundary.

Smaller Gaussian standard deviation stores the edge boundary, but does not capture the whole object of interest.

Solution: Superposing GVF fields with

different Gaussian standard deviations.

Superposition Results

Demo

Final Results

Original Final

Questions