A test for multiple signal detection from noisy images

Abstract: Gaussian random field theory has been extensively used to model the brain images. In this work, I use the reproducing kernel Hilbert space (RKHS) machinery to derive the likelihood ratio test statistic for activation signal detection in functional magnetic resonance imaging. The models considered have the form of smoothed version of signal plus a white noise which include scale and rotation space random fields with one or more signals as special cases.

machine learningprobabilitystatistics theory

Audience: researchers in the topic

Comments: Khalil Shafie Holighi is professor of statistics at University of Northern Colorado.

Gothenburg statistics seminar

Series comments: Gothenburg statistics seminar is open to the interested public, everybody is welcome. It usually takes place in MVL14 (http://maps.chalmers.se/#05137ad7-4d34-45e2-9d14-7f970517e2b60, see specific talk).