| Input: Lung CT image. |
| Output: Edge detected image using Variant ACO algorithm. |
| begin |
| feed the otsu algorithm output as the input for Ant Colony Optimization algorithm |
| apply Ant Colony Optimization algorithm to the image |
| the output image is the output of logical Ant Colony Optimization algorithm |
| end |
| (a) Otsu algorithm |
| Input: Lung CT image. |
| Output: Intermediate result—Edge detected and segmented image |
| begin |
| for each pixel of image |
| find weight of pixel for both foreground and background |
| find mean of weights for both foreground and background |
| find variance of the pixels |
| end for |
| calculate within-class variance for all the pixels |
| maximum value of within-class variance gives the edge pixels. |
| end |
| (b) Ant Colony Optimization algorithm |
| Input: Lung CT image. |
| Output: Edge detected lung CT image. |
| begin |
| for each pixel of image |
| find weight of pixel for both foreground and background |
| find mean of weights for both foreground and background |
| find variance of the pixels |
| end for |
| calculate within-class variance for all the pixels |
| maximum value of within-class variance gives the edge pixels. |
| Initialize the base attractiveness, , and visibility, , for each edge |
| for < IterationMax do: |
| for each ant do: |
| choose probabilistically (based on previous equation) the next state to move into; |
| add that move to the tabu list for each ant |
| repeat until each ant completed a solution |
| end for |
| for each ant that completed a solution do: |
| update attractiveness for each edge that the ant traversed |
| end for |
| if (local best solution better than global solution) |
| save local best solution as global solution |
| end if |
| end for |
| end |
