Registration of multimodal bone images based on edge similarity metaheuristic.

OBJECTIVE: Blurry medical images affect the accuracy and efficiency of multimodal image registration, whose existing methods require further improvement.

METHODS: We propose an edge-based similarity registration method optimised for multimodal medical images, especially bone images, by a balance optimiser. First, we use a GPU (graphics processing unit) rendering simulation to convert computed tomography data into digitally reconstructed radiographs. Second, we introduce the improved cascaded edge network (ICENet), a convolutional neural network that extracts edge information of blurry medical images. Then, the bilateral Gaussian-weighted similarity of pairs of X-ray images and digitally reconstructed radiographs is measured. The a balanced optimiser is iteratively applied to finally estimate the best pose to perform image registration.

RESULTS: Experimental results show that, on average, the proposed method with ICENet outperforms other edge detection networks by 20%, 12%, 18.83%, and 11.93% in the overall Dice similarity, overall intersection over union, peak signal-to-noise ratio, and structural similarity index, respectively, with a registration success rate up to 90% and average reduction of 220% in registration time.

CONCLUSION: The proposed method with ICENet can achieve a high registration success rate even for blurry medical images, and its efficiency and robustness are higher than those of existing methods.

SIGNIFICANCE: Our proposal may be suitable for supporting medical diagnosis, radiation therapy, image-guided surgery, and other clinical applications.