On Applying Preprocessing Techniques on Gallbladder Ultrasound Images

Ultrasound imaging is a common and non-invasive method for diagnosing gallbladder diseases, including gallbladder cancer (GBC). However, the inherent challenges of ultrasound images-such as noise, low contrast, and variable image quality-necessitate the use of preprocessing techniques to enhance image clarity and improve diagnostic accuracy. In this study, we apply a series of preprocessing methods, including median filtering, Gaussian filtering, bilateral filtering, adaptive histogram equalization, and wavelet transforms, to gallbladder ultrasound images. We evaluate the effectiveness of these techniques using a variety of quality metrics such as sharpness, signal-to-noise ratio (SNR), entropy, structural similarity index (SSIM), and perceptual image quality evaluator (PIQE). A detailed analysis of the preprocessing methods was conducted, where the performance of each technique was systematically compared. Additionally, we highlight the statistical variations in these metrics across different preprocessing approaches to identify the most suitable methods for enhancing GBC detection. The results show that specific preprocessing methods improve image quality and feature clarity, aiding accurate gallbladder disease detection models. This work is an initial groundwork for enhancing the diagnostic value of ultrasound images for GBC. It suggests future directions for integrating these methods into machine-learning pipelines for automated disease detection.