Generalized Mean-Based Joint Segmentation and Registration Model on High-Noise Multi-Modal Images

Medical imaging plays a critical role in clinical decision-making and patient care. However, the presence of high levels of noise in medical images can significantly impact the accuracy of diagnosis and subsequent analysis. In recent years, joint segmentation and registration models have emerged as an effective alternative approach for enhancing medical images. Nevertheless, traditional methods, such as the Chan-Vese model, face challenges when dealing with images with high levels of noise. To address this limitation, this paper introduces a different approach that incorporates generalized mean into the joint model. Our joint model combines the generalized mean-based image segmentation which utilizes the fuzzy-membership function, modified normalized gradient fields and linear curvature for registration task. The performance of the proposed model is tested on 2D synthetic and real medical images with and without the presence of the white Gaussian noise. Then it is compared to the existing joint model using three evaluation criterions which are Dice coefficient metric, registration value and computational time. The proposed joint model improved by 60% according to the numerical results when tested on images with high level of noise. The model is useful and beneficial to the radiologists to perform quantitative analysis in assessing disease progression, response to treatment, and overall patient health.