With the rapid growth of the digital imaging, image processing techniques are widely involved in many industrial and medical applications.Image thresholding plays an essential role in image processing and computer vision applications.It has a vast domain of usage.Areas such document image analysis, scene or map processing, satellite imaging and material inspection in quality control tasks are examples of applications that employ image thresholding or segmentation to extract useful information from images. Medical image processing is another area that has extensively used image thresholding to help the experts to better interpret digital images for a more accurate diagnosis or to plan treatment procedures.Opposition-based computing, on the other hand, is a recently introduced model that can be employed to improve the performance of existing techniques.In this thesis, the idea of oppositional thresholding is explored to introduce new and better thresholding techniques. A recent method, called Opposite Fuzzy Thresholding (OFT), has involved fuzzy sets with opposition idea, and based on some preliminary experiments seems to be reasonably successful in thresholding some medical images.In this thesis, a Weighted Opposite Fuzzy Thresholding method (WOFT) will be presented that produces more accurate and reliable results compared to the parent algorithm.This claim has been verified with some experimental trials using both synthetic and real world images.Experimental evaluations were conducted on two sets of synthetic and medical images to validate the robustness of the proposed method in improving the accuracy of the thresholding process when fuzzy and oppositional ideas are combined.
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