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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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