Computer Science

Presented ByGolam Morshed Maruf

Red Blood Cell Image segmentation based on Ant Colony Optimization

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Outline

• Introduction.• Natural behavior of ant.• Edge detection Model.�• ACO based edge Detection.• Experimental Result

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Introduction

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Introduction

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Introduction

Introduction

How Ants Move

Actually ??

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Introduction

Overview: In the real world, ants (initially) wander randomly, and upon

finding food return to their colony while laying down pheromone trails.

Over time, however, the pheromone trail starts to evaporate, thus reducing its attractive strength.

In a short path pheromone density remains high. Thus, when one ant finds a good (i.e., short) path from the

colony to a food source, other ants are more likely to follow that path.

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Natural behavior of ant

Ant Algorithms – (P.Koumoutsakos – based on notes L. Gamberdella (www.idsia.ch)

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Natural behavior of ant

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Edge Detection Model

ACO algorithm

Initialize

SCHEDULE_ACTIVITIESConstruct Ant SolutionsDo Daemon Actions (optional)Update Pheromones

END_SCHEDULE_ACTIVITIES

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Edge Detection ModelInitialize:Set the parameters and assigning the initial pheromone value.

Schedule Activities:1. Construct Ant Solutions:

• Here, τij(t) represents quality of pheromone on the edge.

• ηij represents the heuristic information.

otherwise

allowedkit

t

tpk

allowedkijij

ijij

kij

k

0

f)(

)(

Where to GO??

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Edge Detection Model2. Do Daemon Actions: Performed by multiple ants to improve the solution or search process.3. Update Pheromones: The goal of the pheromone update is to increase the

pheromone values associated with good solutions and decrease those associated with bad ones.

Update is done by: τij(t + n) = р × τij(t) + ∆ τij

here p, is pheromone evaporation rate and ∆ τij is the quantity of pheromone laid on edge.

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ACO-based Image Edge Detection

0,0 1,0 2,0 W-1,0

0,1 1,1 2,1 W-1,1

0,h-1 1,h-1 2,h-1 W-1,h-1

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ACO-based Image Edge Detection

• A pixel is connected to every pixel that touches one of its edges or corners.

• An ant cannot move to a pixel if it is not connected to the pixel where the ant is

currently located.

• An ant can move only to an adjacent pixel.

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ACO-based Image Edge Detection

• Artificial ants are distributed over the image.• The goal is to construct a final pheromone matrix that reflects

the edge information.• Each element in the pheromone matrix corresponds to a

pixel in the image and indicates whether a pixel is an edge or not.

i-1,j-1 i-1,j i-1,j+1

i,j-1 i,j i,j+1

i+1,j-1 i+1,j i+1,j+1

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ACO-based Image Edge Detection1. Initialization Process :• K ants are assigned random positions in the M1 X M2 image.• The initial value of each element in the pheromone matrix

is set to a constant τinit.

• The heuristic information at pixel (i,j) is determined by the local statistics at that position:

• Here Ii,j is the intensity value at (i,j), and

max

,,

)(

v

Iv jicji

1,1,1,11,1,1,11,11,1, )( jijijijijijijijijic IIIIIIIIIv

i-1,j-1 i-1,j i-1,j+1

i,j-1 i,j i,j+1

i+1,j-1 i+1,j i+1,j+1

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ACO-based Image Edge Detection

2. Iterative Construction and Update Process:• On every iteration, an ant moves from the pixel to an

adjacent pixel according to the pseudorandom proportional rule.

• Each time an ant visits a pixel, it immediately performs a local update on the associated pheromone.

• The amount of pheromone on the pixel on the iteration, is updated based on the equation for ACS local pheromone update.

initnji

nji .).1( )(

,)(

,

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ACO-based Image Edge Detection

2. Iterative Construction and Update Process:After all the ants finish the construction process, global pheromone update is performed on pixels that have been visited by at least one ant:

Here, is the amount of pheromone deposited by each ant on each pixel.

)(,

)1(,

)(, 1

.).1( Kji

nji

nji K

K

)(,Kji

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ACO-based Image Edge Detection3. Decision Process:• The final pheromone matrix is used to classify each pixel either as an

edge or a non-edge.• The decision is made by applying a threshold on the final pheromone

matrix.

Do initialization proceduresfor each iteration n = 1:N do

for each construction_step l = 1:L dofor each ant k = 1:K doSelect and go to next pixelUpdate pixel’s pheromone (local)

endendUpdate visited pixels’ pheromones (global)

end

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Experimental Results

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Experimental Results

Experimental Results

Thank You

CALIC