;;A Muti-Agents system for the Images Segmentation

;;Author: KHOUADJIA Mustapha Redouane 
;;Affiliation: Computer Science Institute, Constatine university , Algeria. 
;;Email:khouadjia@yhaoo.fr

globals [it nbrc1 nbrc2 nbrc3 nbrc4]
turtles-own [Diffusion_Vector age  age_rep stat  family fitness_list successful_family  U V  M1 M2  label_color  delta ]
patches-own [cpixel  ]
breed [ classe1 ]
breed [ classe2 ]
breed [ classe3 ]
breed [ classe4 ]
breed [ classe5 ]
breed [ classe6 ]

;;-----------------------------------------------------------------------------------------------------------------------
to setup_criterion [a b c d x LB]  ;; Initialization of the parameters
  while [any? other-turtles-here with [breed = breed-of myself]]
  [   setxy random world-width
        random world-height
     ] 
      set  U a
      set  V b 
      set M1 c
      set M2 d
      set delta x
      set label_color LB
    end
;;-----------------------------------------------------------------------------------------------------------------------
to-report intensity_difference[a ]         ;;Calculate the difference of intensity between two pixels
ifelse abs ( cpixel-of a  - cpixel-of patch-here )<= delta [report 1][report 0]
end
;;-----------------------------------------------------------------------------------------------------------------------
to-report Distribution_Density  ;; Calculation of the distribution density 
locals [D]
foreach values-from neighbors [self] [ set D (D + intensity_difference ?) ]
report D
end  
;;-----------------------------------------------------------------------------------------------------------------------
to-report  Regional_average  ;; Calculation of the Regional_average
 locals[patches_region]
  ask patch-here [set patches_region   patches in-radius R_region ]
  report mean values-from patches_region [cpixel]
end
;;-----------------------------------------------------------------------------------------------------------------------
 to-report std  ;;  Calculation of the regional standard deviation 
 locals[patches_region ] 
 ask patch-here[set patches_region  patches in-radius R_region ]
  report standard-deviation values-from patches_region [cpixel]
end 
;;-----------------------------------------------------------------------------------------------------------------------
to-report fitness [agent]         ;; Calculation of the value of fitness
ifelse (turtle agent != nobody and stat-of turtle agent = "parent")
[report 1 - (age_rep-of turtle agent / life_span )]
[report -1]
end
;;-----------------------------------------------------------------------------------------------------------------------

 to selection_of_successeful_family  ;; Selection of the best agents (the most adapted)
 set successful_family []
set fitness_list  (map [list (?1) (?2)] family   map [ fitness ? ] family )
 let i 0
while [i < length fitness_list ]
 [ if (( item 1 (item i fitness_list) >= 0 )) 
  [set successful_family   lput (item 0 (item i fitness_list))  successful_family]
   set i i + 1
 ]  
end 
;-----------------------------------------------------------------------------------------------------------------------
to Maj_vecteurs ;; The Update of the behavioral vectors
 locals[i]
 set Diffusion_Vector []
 set i 0
while [i < 8] 
[ set Diffusion_Vector   lput (1 / (length successful_family) * (sum map [ item i Diffusion_Vector-of turtle ? ] successful_family)) Diffusion_Vector
    set i i + 1 ] 
  
end
;;-----------------------------------------------------------------------------------------------------------------------
to-report stimulus_local        ;; Activation of the local stimulus
ifelse ((pcolor-of patch-here >= 0 and pcolor-of patch-here <= 9.99) and (Distribution_Density >= U and Distribution_Density <= V))
[ifelse (breed = classe1)or ((Regional_average >= M1 and Regional_average <= M2 ));; and (std >= S1 and std <= S2))
  [report (true)][report(false)]]
    [report (false)]
 end
;;-----------------------------------------------------------------------------------------------------------------------
to-report  Direction [vect]   ;; Select a direction for a given behavior

 let liste []
 let  i 0
 while [i < 8]
  [  if ((item i vect) =  max vect )
                   [set liste lput i liste]
      set i i + 1  ]
  report one-of liste
end
;;-----------------------------------------------------------------------------------------------------------------------
to Diffusion       ;;  diffusion behavior
let DD Direction Diffusion_Vector
set Diffusion_Vector  replace-item DD ( n-values 8 [0] )1
set heading (DD + 1)* 45
jump (random(5))
set age age + 1
if(stat = "young") 
 [set stat "adult"]
end
;;-----------------------------------------------------------------------------------------------------------------------
 to Extinction
 if  (age > life_span)
       [die]
   end
;;-----------------------------------------------------------------------------------------------------------------------
to Reproduction   ;; reproduction behavior
if (breed != classe1)
[set heading random(5)* 45 ]
let tempo_liste [] 
hatch Nbr_offspring [  set heading-of self one-of list (heading-of myself - random 45) (heading-of myself + random 45) 
                       Migration                  
                       set Diffusion_Vector n-values 8 [1 / 8]
                       set age 0
                       set age_rep 0
                       set stat "young"
                       set tempo_liste lput (who-of self) tempo_liste ]
                     
  foreach tempo_liste [set family-of turtle ? (fput who (remove ? tempo_liste ))]  
  set stat "parent" 
  set age_rep age
  set age life_span 
   end
;;-----------------------------------------------------------------------------------------------------------------------
to Migration ;;  migration behavior

if(any? other-turtles-here  with [(breed = breed-of myself )])
        [ jump (random(step_migration))]
          
          set age age + 1  
  end
 ;;-----------------------------------------------------------------------------------------------------------------------
 to labeling   ;;Labeling 
    set pcolor label_color
    hideturtle 
  end

 ;;-----------------------------------------------------------------------------------------------------------------------
to distribution  ;; Creat and distribute the various categories of agents 

 create-custom-Classe1 NBR_CLASS1 [ setup_criterion 0 5 0 0  0.80  red] 
 create-custom-Classe2 NBR_CLASS2 [ setup_criterion 6 8 0 5.51  0.99 blue] 
 create-custom-Classe3 NBR_CLASS3 [ setup_criterion 6 8 5.52  7.20  0.99 yellow]
 create-custom-Classe4 NBR_CLASS4 [ setup_criterion 6 8 7.21 9.50  0.99  green] 

 ask turtles [initialisation] 
end
;;-----------------------------------------------------------------------------------------------------------------------
to initialisation    ;;   Initialization of the parameters
set Diffusion_Vector n-values   8  [1 / 8] 
set family  []
set stat "young"
set age 0
set it 0

end  
;;-----------------------------------------------------------------------------------------------------------------------
to go    ;; The Main of a program 


 
   set it it + 1
   output-show  count turtles
   ask turtles [
    ifelse(stat != "parent")
     [
     if ((not empty? family) and (stat !="young"))
        [selection_of_successeful_family
         Maj_vecteurs  ] 
 
      ifelse  (stimulus_local)
         [Reproduction 
          labeling]
          [ diffusion 
          Extinction]
        
           ] 
        [ifelse (age <= (2 * life_span))
            [ set age age + 1] 
            [die]]
            ]  
  
     
end 
;;-----------------------------------------------------------------------------------------------------------------------
to import-pgm [pathname]  
   ;; import PGM format image 
  ;; USER-CHOOSE-FILE returns false if the user cancels, so this
  ;; check is here to handle that case
  clear-all
  if not is-string? pathname
    [ stop ]
  cp ct
  carefully [
    file-open pathname
    import-pgm-contents
  ] 
  ;; error handler for "carefully"
  [
    user-message (word pathname
                       " is not a valid ASCII format PGM file: "
                       error-message)
  ]
  
  file-close
  ask patches [set cpixel pcolor ]
 
end
;;-----------------------------------------------------------------------------------------------------------------------
to import-pgm-contents
  ;; check magic number
  if read-pgm-entry != "P2" [
    user-message "This is not a valid ASCII format PGM file (magic number is not P2)"
    stop
  ]
  ;; get width, height, and white value
  let width read-from-string read-pgm-entry
  let height read-from-string read-pgm-entry
  let max-value read-from-string read-pgm-entry
  ;; read the actual pixel values
  let x 0
  let y 0
  while [y < height] [
    set x 0
    while [x < width]
      [ let value read-from-string read-pgm-entry
        ;; convert from image coordinates to patch coordinates
        ;; (in patch coordinates, the origin is in the center,
        ;; not the top left, and y coordinates increase going up,
        ;; not going down)
        let px x + min-pxcor
        let py max-pycor - y
        ;; make sure we're not out of bounds
        if (abs px <= max-pxcor) and (abs py <= max-pycor) [
          ;; actually color the patch
          ask patch px py [
            set pcolor scale-color white value 0 max-value
          ]
        ]
        set x x + 1
      ]
    set y y + 1
  ]

end
;;-----------------------------------------------------------------------------------------------------------------------
;; reads the next entry in a pgm file 
;; - an error occurs if there are no more entries
;; - entries are separated by arbitrary whitespace 
;; - characters on a line after "#" are comments
to-report read-pgm-entry
  ;; get next character
  let c file-read-characters 1
  ;; ignore leading whitespace
  while [whitespace-char? c]
    [ set c file-read-characters 1 ]
  ;; skip comments
  if c = "#" [
    ;; first skip the comment itself
    while [c != "\n" and c != "\r"] [
      set c file-read-characters 1
    ]
    ;; then skip linefeeds and/or newlines at end of comment
    while [c = "\n" or c = "\r"] [
      set c file-read-characters 1
    ]
  ]    
  ;; read the entry
  let str ""
  while [not whitespace-char? c]
    [ set str str + c
      set c file-read-characters 1 ]
  report str
end
;;-----------------------------------------------------------------------------------------------------------------------
;; reports true if c is a single whitespace character
to-report whitespace-char? [c]
  report c = " " or
         c = "\t" or   ;; tab
         c = "\n" or   ;; newline
         c = "\r"      ;; linefeed
end
@#$#@#$#@
GRAPHICS-WINDOW
197
10
720
554
256
256
1.0
0
10
1
1
1
0
1
1
1
-256
256
-256
256

CC-WINDOW
5
568
959
663
Command Center
0

BUTTON
5
55
186
88
import  image
import-pgm user-file
NIL
1
T
OBSERVER
T
NIL

TEXTBOX
10
142
148
160
Functions

TEXTBOX
10
29
100
47
Import

BUTTON
46
320
141
353
clear-all
clear-all
NIL
1
T
OBSERVER
T
NIL

BUTTON
43
175
135
208
NIL
distribution
NIL
1
T
OBSERVER
T
NIL

BUTTON
49
270
141
303
clear-Agents 
clear-turtles 
NIL
1
T
OBSERVER
T
NIL

BUTTON
45
220
138
253
NIL
go
T
1
T
OBSERVER
T
NIL

SLIDER
770
126
942
159
Life_span
Life_span
1
10
3
1
1
NIL

TEXTBOX
800
10
950
28
 Global Variables 

TEXTBOX
798
237
948
255
Classes of agents

SLIDER
770
259
942
292
NBR_CLASS1
NBR_CLASS1
0
100
20
5
1
NIL

SLIDER
770
304
942
337
NBR_CLASS2
NBR_CLASS2
0
100
20
5
1
NIL

SLIDER
771
348
943
381
NBR_CLASS3
NBR_CLASS3
0
100
20
5
1
NIL

SLIDER
771
393
943
426
NBR_CLASS4
NBR_CLASS4
0
100
20
5
1
NIL

SLIDER
767
42
939
75
R_region
R_region
0
10
2
1
1
NIL

MONITOR
45
418
150
467
Nbr of  iterations
it
3
1

MONITOR
38
486
171
535
Nbr of active agents 
count turtles with [stat != "parent"]
3
1

SLIDER
769
84
941
117
Nbr_offspring
Nbr_offspring
0
100
2
1
1
NIL

SLIDER
773
178
945
211
step_migration
step_migration
1
20
5
1
1
NIL

@#$#@#$#@

A MULTI-AGENTS SYSTEM FOR THE IMAGES SEGMENTATION


WHAT IS IT ?
This program presents an adaptative approach based on a multi-agents system for the images segmentation.  This system is constituted at the micro level of autonomous entities, which deployed on the image. They are equipped with a capacity to estimate the homogeneity of a region from their current locality. Each entity exhibits several behaviours in response to the local stimulus. It can migrate, reproduce, or diffuse within the image.  Various entities explore the image and label the pixels when they belong to homogeneous segments. The interactions at the micro level allow the emergence of a new feature that is segmentation of the image. 


HOW TO USE IT 

1. Import an image to the PGM format(Image in level of grey) and fit the screen to the dimensions of this one
2. Fix the number of segmentation agents for every class (4 classes by default)
3. Adjust the perception radius of the agents R_region, Their life span, As well as  	   the number of offspring which they can generate during the self reproduction
4. Adjust the step of migration. This step correspond to the dispersal amplitude 	   of the segmentation agents
5. Create and distribute the segmentation agents on the image with the button "Distribution"
6.Throw the program with the button “go”




THING TO NOTICE

Each entity is equipped with an ability to estimate the homogeneity of a region from its locality. When an inhomogeneous segment is met, the autonomous entity diffuses itself towards a nearby pixel by taking a direction which it will have to select.
In case the entity locates a homogeneous segment, this last one, through the self reproduction behaviour, delivers a certain number of offspring in its local region, and labels the pixel on which it resides.  
The new offspring will have the task to label the same type of segment as the parent entity.
In its birth, the offspring presents a behaviour of migration, if ever there is a strong concentration of congeners in its local region. A migration can also arise, in case, the pixel on which is the entity is already labelled.  
Each entity has a predefined life span. When an agent takes age, it gains in maturity. This maturity is indicated by its current state (young, adult, parent, old). 
It will help it to improve and acquire during its life of the knowledge, allowing it to return its search more effective. 
The directions taken by the entity during its diffusion are determined by a vector of directions. The elements of this vector correspond to the probability of success to find a homogeneous segment; if a given direction is selected.
The probabilities are updated by basing itself on the directions taken beforehand by the better adapted entities belonging to the family of the entity.




THING TO TRY

Try to practice various steps of migration using slide.  It will have for consequence of change the density of the entities swarm through the picture.


EXTENDING THE MODEL

Our system remains opened and several points can be studied:

-The possibility of automating some mechanisms of agents segmentation, such as the life span, and the number of generated offspring. The agent can so manage its parameters according to the local constraints to which it is confronted. For example: an agent will limit the number of created offspring, if ever there is a strong concentration of congener in its local region.

-Try to specialize our system for the detection of certain medical abnormalities at the level of Brain MRI. It will be question for example of defining segmentation agents whose role is the detection of tumours at the first stage.


CREDITS AND REFERENCES
Author: KHOUADJIA Mustapha Redouane
Affiliation: Faculty of Engineering, Computer Science Institute, Constatine University, Algeria.

Email:khouadjia@yhaoo.fr

To refer to this model in academic publications, please use: 

M.Khouadjia, S.Meshoul, E-G.Talbi: "A Complex System for the Images Segmentation" in META06, workshop on metaheuristics, Hammamet November 2-4 2006, Tunisia.


NOTES
You can create easily images in the PGM format  by means of the Xnview software :
http://www.xnview.com/








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leaf
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@#$#@#$#@
NetLogo 3.1.3
@#$#@#$#@
import-pgm "sample.pgm"
@#$#@#$#@
@#$#@#$#@
@#$#@#$#@