A Comprehensive Analysis of Harmonizing Classical Approaches with the Cutting-Edge Techniques for Specular Reflection Detection in Medical Imaging

The detection and removal of Specular Reflections (SR) is spotted as a significant challenge in the field of Medical Image Analysis, especially in Medical visual diagnosis for ensuring high quality images in accurate diagnosis. These reflections appear as bright spots in images, obscuring the critical tissue details and leads to mis-diagnosis. SR detection has attracted huge focus in recent years, as researchers aim to build impressive algorithms with improved performance and reduced complexity. By inculcating appropriate techniques, their impact can be minimized and consequently lead to better visualization of anatomical and pathological details, which are the prerequisite for medical diagnosis. The aim of this article is to project the various approaches adopted and the challenges in classical image processing approaches and deep learning-based techniques in SR detection and highlighting the importance of hybrid methods with their underlying principles, by comprehending the articles from various scholarly publishing platforms.