Image Processing Has Several Applications In Areas Such As Medicine, Industry, Agriculture, Etc. Most Of The Methods Of Image Processing Require A First Step Called Segmentation. Segmentation Is One Of The Most Important Tasks In Image Processing. It Consists Of Classifying The Pixels Into Two Or More Groups Depending On Their Intensity Levels And A Threshold Value. The Quality Of The Segmentation Depends On The Method Applied To Select The Threshold. When The Image Is Segmented Into Two Classes, The Task Is Called Bi-level Thresholding (BT) And It Requires Only One Th Value. On The Other Hand, When Pixels Are Separated Into More Than Two Classes, The Task Is Named As Multilevel Thresholding (MT) And Demands More Than One Th Value. The Use Of The Classical Implementations For Multilevel Thresholding Is Computationally Expensive Since They Exhaustively Search The Best Values To Optimize The Objective Function. Under Such Conditions, The Use Of Optimization Evolutionary Approaches Has Been Extended. The Electromagnetism-like Algorithm (EMO) Is An Evolutionary Method Which Mimics The Attraction–repulsion Mechanism Among Charges To Evolve The Members Of A Population. Different To Other Algorithms, EMO Exhibits Interesting Search Capabilities Whereas Maintains A Low Computational Overhead. In This Paper, A Multilevel Thresholding (MT) Algorithm Based On The EMO Is Introduced
