Cutting-Edge Techniques for Enhanced Kidney Stone Detection in Ultrasound Imaging

The identification of regions of interest (ROIs) in ultrasound images is a complex task due to the varied textures and inherent speckle noise of such images. Ultrasound scanning is widely utilized for diagnosing abnormalities, including kidney stones, where accurate detection is crucial. This research addresses the challenges associated with automatic object detection in ultrasound images, focusing on enhancing kidney stone recognition. An application has been developed to assist medical practitioners in pinpointing stone regions within ultrasound images through a semi-automatic approach. In this system, practitioners first select the regions of interest for analysis, followed by the application of feature extraction techniques to these selected areas. The techniques employed include Contrast Limited Adaptive Histogram Equalization (CLAHE), Gabor filters, and Active Contour Models (ACMs). CLAHE improves image contrast and detail, Gabor filters capture texture information, and ACMs provide precise object delineation and segmentation. Experimental results demonstrate that the integration of these three methods offers a significant improvement in kidney stone detection compared to using each technique independently. The combined application of CLAHE, Gabor filters, and ACMs leverages the strengths of each method, resulting in enhanced accuracy and reliability in identifying stone regions. This approach aims to advance diagnostic precision and enable timely interventions for patients with kidney stones. Ongoing refinements and validations of this methodology are essential to further enhance its robustness and practical applicability in clinical settings, ultimately contributing to better patient outcomes.