Quantitative Evaluation of Kidney and Gallbladder Stones by Texture Analysis Using Gray Level Co-Occurrence Matrix Based on Diagnostic Ultrasound Images

Background/Objectives: Accurate diagnosis during ultrasound examinations of patients with kidney and gallbladder stones is crucial. Although stone areas typically show posterior acoustic shadowing on ultrasound images, their accurate diagnosis can be challenging if the shaded areas are vague. This study proposes a method to improve the diagnostic accuracy of kidney and gallbladder stones through texture analysis of ultrasound images. Methods: Two doctors and three sonographers evaluated abdominal ultrasound images and categorized kidney and gallbladder stones into groups based on their predicted likelihood of being present: 50-60%, 60-80%, and >= 80%. The texture analysis method for the posterior acoustic shadows generated from ultrasound images of stones was modeled using a gray level co-occurrence matrix (GLCM). Average values and 95% confidence intervals were used to evaluate the method. Results: The three prediction classes were clearly distinguished when GLCMContrast was applied to the ultrasound images of patients with kidney and gallbladder stones. However, GLCMCorrelation, GLCMEnergy, and GLCMHomogeneity were found to be difficult for analyzing the texture of shadowed areas in ultrasound images because they did not clearly or completely distinguish between the three classes. Conclusions: Accurate diagnosis of kidney and gallbladder stones may be possible using the GLCM texture analysis method applied to ultrasound images.