Souham Meshoul

Work place: College of Engineering, MISC laboratory, CICS Group, Department of Computer Science, University Mentouri – Constantine, Algeria



Research Interests: Image Manipulation, Pattern Recognition, Artificial Intelligence, Bioinformatics


MESHOUL Souham, received the state engineer degree, master degree and State doctorate degree in computer science from Mentouri University in Constantine Algeria. She is currently an associate professor at the computer science department and a researcher at MISC laboratory Constantine City. Her current interests include computational intelligence and its applications, complex systems, bioinformatics, pattern recognition and image analysis and understanding

Author Articles
A Fully Adaptive and Hybrid Method for Image Segmentation Using Multilevel Thresholding

By Salima Ouadfel Souham Meshoul

DOI:, Pub. Date: 8 Jan. 2013

High level tasks in image analysis and understanding are based on accurate image segmentation which can be accomplished through multilevel thresholding. In this paper, we propose a new method that aims to determine the number of thresholds as well as their values to achieve multilevel thresholding. The method is adaptive as the number of thresholds is not required as a prior knowledge but determined depending on the used image. The main feature of the method is that it combines the fast convergence of Particle Swarm Optimization (PSO) with the jumping property of simulated annealing to escape from local optima to perform a search in a space the dimensions of which represent the number of thresholds and their values. Only the maximum number of thresholds should be provided and the adopted encoding encompasses a continuous part and a discrete part that are updated through continuous and binary PSO equations. Experiments and comparative results with other multilevel thresholding methods using a number of synthetic and real test images show the efficiency of the proposed method.

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