title: Towards measuring neuroimage misalignment.
abstract: To enhance neuro-navigation, high quality pre-operative images must be registered onto intra-operative configuration of the brain. Therefore evaluation of the degree to which structures may remain misaligned after registration is critically important. We consider two Hausdorff Distance (HD)-based evaluation approaches: the edge-based HD (EBHD) metric and the Robust HD (RHD) metric as well as various commonly used intensity-based similarity metrics such as Mutual Information (MI), Normalised Mutual Information (NMI), Entropy Correlation Coefficient (ECC), Kullback-Leibler Distance (KLD) and Correlation Ratio (CR). We conducted the evaluation by applying known deformations to simple sample images and real cases of brain shift. We conclude that the intensity-based similarity metrics such as MI, NMI, ECC, KLD and CR do not correlate well with actual alignment errors, and hence are not useful for assessing misalignment. On the contrary, the EBHD and the RHD metrics correlated well with actual alignment errors; however, they have been found to underestimate the actual misalignment. We also note that it is beneficial to present HD results as a percentile-HD curve rather than a single number such as the 95-percentile HD. Percentile-HD curves present the full range of alignment errors and also facilitate the comparison of results obtained using different approaches. Furthermore, the qualities that should be possessed by an ideal evaluation metric were highlighted. Future studies could focus on developing such an evaluation metric.
authors.affiliation: Intelligent Systems for Medicine Laboratory, The University of Western Australia, Perth, Australia.
authors.forename: Revanth Reddy
authors.initials: RR
authors.lastname: Garlapati
authors.affiliation: Intelligent Systems for Medicine Laboratory, The University of Western Australia, Perth, Australia.
authors.forename: Ahmed
authors.initials: A
authors.lastname: Mostayed
authors.affiliation: Intelligent Systems for Medicine Laboratory, The University of Western Australia, Perth, Australia.
authors.forename: Grand Roman
authors.initials: GR
authors.lastname: Joldes
authors.affiliation: Intelligent Systems for Medicine Laboratory, The University of Western Australia, Perth, Australia.
authors.forename: Adam
authors.initials: A
authors.lastname: Wittek
authors.affiliation: Intelligent Systems for Medicine Laboratory, The University of Western Australia, Perth, Australia; Centre for Cardiovascular Science, The University of Edinburgh, Edinburgh, United Kingdom.
authors.forename: Barry
authors.initials: B
authors.lastname: Doyle
authors.affiliation: Intelligent Systems for Medicine Laboratory, The University of Western Australia, Perth, Australia; Institute of Mechanics and Advanced Materials, School of Engineering, Cardiff University, Cardiff, Wales, United Kingdom. Electronic address: karol.miller@uwa.edu.au.
authors.forename: Karol
authors.initials: K
authors.lastname: Miller
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods* / Q000592:standards*; D008279:Magnetic Resonance Imaging; D059906:Neuroimaging / Q000379:methods* / Q000592:standards*
keywords: Brain deformation; Hausdorff Distance; Image similarity metrics; Intra-operative registration; Non-rigid registration