Comparison of the existing tool localisation methods on two-dimensional ultrasound images and their tracking results

Over the last decade, different micro-tool navigation and localisation algorithms have been developed. They can be divided into three groups: the eigen-decomposition methods such as principal component analysis (PCA), the transform methods such as Hough transform (HT) and the space random iteration methods such as the random sample consensus (RANSAC) algorithm. To suppress the speckle noise of the ultrasound image, different noise suppression methods are also proposed. In this article, different combinations of preprocessing methods and localisation methods are compared. A tracking system is also developed to adapt these localisation methods to a dynamic situation. So far, the line-filter method has achieved the best contrast ratio, and the threshold method requires the shortest time. These two preprocessing methods are proposed in the localisation algorithm and the tracking system. Simulations and experiments were conducted to verify the combinations of the localisation and tracking results. In static localisation, the line-filter+RANSAC method achieves the highest localisation accuracy. In the dynamic situation, the PCA method achieves the highest tracking accuracy. In the realtime evaluation, the calculation time of the RANSAC algorithm is the shortest. To satisfy the demand for localisation accuracy and calculation time, the line-filter+RANSAC algorithm is the best choice.