Enhancing Diagnostic Visualization of Ultrasound Images Using Guided Adaptive Contrast Enhancement

Digital images play a vital role in various domains, serving as crucial sources of information. From medical science to transportation management, their applications are diverse and extensive. However, ensuring consistently high-quality images poses a significant challenge due to various acquisition factors. In this study, we address the issue of image enhancement, focusing on contrast enhancement techniques. While histogram equalization has been a popular choice, we introduce Guided Adaptive Contrast Enhancement (GACE) as an advanced method for improving image quality. Ultrasound imaging often suffers from low-quality images due to equipment limitations or improper setup. Such images pose challenges in interpretation and analysis. To tackle this, we propose utilizing GACE to enhance ultrasound images effectively. Unlike traditional methods, GACE offers adaptive contrast enhancement, which adjusts to the local characteristics of the image. This approach ensures superior results, especially in scenarios where traditional methods may fall short. Through our research, we demonstrate the efficacy of GACE in enhancing ultrasound images, providing clearer and more interpretable results. Additionally, we conduct a comparative analysis with existing techniques, highlighting the advantages of GACE in image processing. By leveraging advanced techniques like GACE, we aim to not only improve image quality but also enhance the capabilities of operators, radiologists, and other stakeholders in various domains reliant on digital image data.