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Earlier 1D histogram-based entropic methods for multilevel image thresholding suffer from the lack of contextual information. Subsequently, the idea was extended for 2D histogram-based methods, where neighbourhood pixels were considered to retain the contextual information. Nevertheless, 2D histogram-based entropic methods are computationally intensive. Moreover, these methods are based on the maximization of entropic functions using an optimizer, leading to less accuracy. To address these issues, we propose a context-sensitive entropy dependency (CSED) based multilevel thresholding method. A new optimizer called opposition equilibrium optimizer (OEO) is introduced. The opposition based learning and escaping strategy are incorporated to enhance exploration capability. Here, 31 test functions including 8 from standard testbed IEEE CEC 2014 are used for validation. The merits are – i) reduced …