Multispectral Image Analysis for Patient Tissue Tracking During Complex Interventions
Francesca Manni, Marco Mamprin, Svitlana Zinger, Caifeng Shan, Ronald Holthuizen, Peter H. N. de With · 2018
During complex interventions, patient tracking is needed for optimal motion compensation, in order to guide the physician in a minimally invasive way. Nowadays, optical tracking systems are used for tracking of markers. Despite the unobtrusiveness of this technology, the approach is cumbersome because it requires manual placement of markers, which can alter during the operations due to the presence of liquids. To improve the clinical workflow, a new feature tracking algorithm is designed, involving feature detection and tracking without optical markers. The new markers are created with the multispectral imaging. Maximally stable extremal regions (MSER) and Speeded Up Robust Feature (SURF) methods are applied to design features and track natural landmarks, e.g. moles and veins. Both methods are tested and compared in accuracy with the mean shift tracking method. SURF reaches the highest accuracy of 0.257 pixels for images at 430 nm and 0.562 pixels for images at 970 nm. This study shows that incorporating multispectral imaging in the surgical scenario leads to an attractive benefit minimizing the risk of marker obstruction and displacement.