Active Contours Technique in Retinal Image Identification of the Optic Disk Boundary
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Transcript of Active Contours Technique in Retinal Image Identification of the Optic Disk Boundary
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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
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The Problem
Objective: Using active contours to find the optic disk boundary.
Impediments: Large image size Location of optic disk Noise Initialization
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Solution Model
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Image Pre-processing Significance: Without pre-
processing the active contours is strongly influenced by noise.
Phases: Thresholding Windowing Morphological techniques
Dilation Erosion Reconstruction
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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.
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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.
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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.
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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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is the snakes’ elasticity
is the snakes’ regidity
Gaussian function’s standarddeviation
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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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xq
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is a regularization parameter
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Experiment Traditional snakes Before & After
Preprocessing Initialization Superposition of
GVF fields Results
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Initialization Incorrect initialization
leads to inaccurate results.
Example: Snake initialized in an
empty GVF field. Results in snake resting
in same area.
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Before & After Pre-processing
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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.
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Superposition Results
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Demo
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Final Results
Original Final
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Questions