Analysis and Detection of Gout Lesions using Image Processing Technique in Spine CT Images Acquired with Dual Energy

Abstract

In this study, I propose an image processing approach for detecting and visualizing gout lesions in spinal images obtained using dual-energy computed tomography (DECT) at low (80 kVp) and high (140 kVp) energy levels. Through gray-level co-occurrence matrix (GLCM) analysis, I observed significant differences in contrast and energy values between the two energy levels, while homogeneity and correlation remained relatively unchanged. Discrete Wavelet Transform (DWT) analysis further revealed that the 80 kVp images displayed a broad distribution of similar pixel intensities, whereas the 140 kVp images exhibited concentrated high-intensity regions, particularly in bony structures. To validate my findings, I trained a neural network using the Levenberg–Marquardt algorithm. I used the high-frequency components extracted from DWT as input features and the intensity values from both energy levels as target outputs. The fitting results showed a strong alignment between the predicted and actual values, confirming the accuracy of the model. The method effectively identified gout lesions and clearly visualized monosodium urate (MSU) crystal deposition. I believe this approach offers a valuable contribution to improving diagnostic accuracy in spinal gout by enabling detailed lesion localization and material differentiation using conventional DECT. Furthermore, it may serve as a useful tool for monitoring therapeutic response and evaluating treatment efficacy.