Treffer: Quantum edge detection of medical images using novel enhanced quantum representation and hill entropy approach.

Cutting-edge medical image analysis, driven by quantum-based techniques, offers automated information extraction from images, revolutionizing health care. Traditional methods are being outpaced by the demand for advanced real-time digital image processing. This article introduces an innovative approach to medical image edge detection based on entropy. In recent years, various quantum representation models have emerged, addressing the complex nature of medical images characterized by dark backgrounds and low contrast. To enhance image quality, the article introduces the novel enhanced quantum representation model, which leverages the colour operations of Caraiman's quantum image representation model to improve the greyscale values of individual pixels. However, the article acknowledges that quantum noise remains a challenge in image processing due to statistical fluctuations in medical imaging. To combat this, the article introduces a neural network-based hybrid filter, comprising neural edge enhancers and bilateral filters. The neural filter acts as a fusion operator, effectively eliminating quantum noise from the output image. Another challenge addressed in this work is the time complexity of edge detection. The article presents a novel methodology for edge extraction based on Hill entropy for medical images, which involves segmenting the image into objects and backgrounds using a threshold value. This method aims to reduce computation time while producing high-quality edge detection. The proposed algorithm is implemented using MATLAB software and evaluated on various images. The results demonstrate the algorithm's effectiveness, with a notably higher peak signal-to-noise ratio of 41.5312%, a lower mean square error of 0.0214%, and an improved contrast-to-noise ratio of 42.59%. These outcomes underscore the algorithm's superior performance in edge detection for medical images, offering a remarkable accuracy of 97.5% compared to traditional methods. [ABSTRACT FROM AUTHOR]

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