Probabilistic Ant Colony Optimization for Contour Detection of Psoriasis

Abstract

Psoriasis is characterized by hyperkeratosis and thickening of the epidermal layer followed by an increase in vascularity and infiltration of inflammatory cells to the dermis, as a result of this process the scales appear, erythema and induration. In the field of health, identification and image analysis can be used as a conclusion to support expert decisions such as identification of tumors, cancers or other diseases including Psoriasis. Ant Colony Optimization (ACO) algorithms are the most successful and widely recognized algorithmic techniques based on ant behaviours. These algorithms have been applied to numerous problems; moreover, for many problems ACO algorithms are among the current high performing algorithms. The goal of this research is to develop a detection emphasize on the contour detection of Psoriasis using ACO. By using the parameters of the Pheromone intensity control values (α), the visibility (β), Evaporation coefficient of pheromone intensity (ρ) and the constant Q the results show that this method is can detect the contour an image of psoriasis.