Abstract
Intensity normalization is widely used to remove the confounding effect of global change exhibited in PET or SPECT brain images such that the local activity can be detected. Improper estimate of global change may induce a biased normalization. To separate the global change from local measurements, an iterative method is proposed to identify reference regions that are not affected by the local activity. From more than one hundred predefined anatomical regions, the reference regions are selected based on their intensity similarity between two groups. Weighted least squares regression is used to compute linear intensity transformations to align intensities of corresponding reference regions across all subjects. Studies with simulated data demonstrated that the proposed method performed better in recovering real intensity change comparing with global mean normalization and with Andersson’s data-driven method.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
