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Renyi entropic thresholding algorithm is relatively robust as it is independent of the controlling parameter q and the grey level resolution. The improvement is solely based on the entropy definition, and it has been observed the introduced controlling parameters do not affect its performance. Performance evaluation results of thresholding algorithms using Renyi entropy has shown to improve the measurements, particularly for stratified flow regimes. Static simulated data from Electrical Capacitance Tomography measurement system for annular and stratified flows in multiphase hydrocarbons production has been used. In this article, entropy definitions are analysed to establish their relationship and after that evaluate their performance in image thresholding. In image processing, the entropy concept has been used in developing thresholding techniques based on maximum entropy principles for image segmentation, enhancement and object detection purposes. The complex nature of information has resulted in some proposed entropy definitions.

The concept of entropy as a measure of information has been extensively applied in information theory and related fields.

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