  Motivated by recent work involving the analysis of biomedical imaging data,
we present a novel procedure for constructing simultaneous confidence corridors
for the mean of imaging data. We propose to use flexible bivariate splines over
triangulations to handle irregular domain of the images that is common in brain
imaging studies and in other biomedical imaging applications. The proposed
spline estimators of the mean functions are shown to be consistent and
asymptotically normal under some regularity conditions. We also provide a
computationally efficient estimator of the covariance function and derive its
uniform consistency. The procedure is also extended to the two-sample case in
which we focus on comparing the mean functions from two populations of imaging
data. Through Monte Carlo simulation studies we examine the finite-sample
performance of the proposed method. Finally, the proposed method is applied to
analyze brain Positron Emission Tomography (PET) data in two different studies.
One dataset used in preparation of this article was obtained from the
Alzheimer's Disease Neuroimaging Initiative (ADNI) database.
