UNIVERSITY OF IDAHO

DEPARTMENT OF MATHEMATICS

COLLOQUIUM

 


FALL 2016

Thursday,  October 13, 3:30-4:20 pm, room TLC 022

Refreshments in Brink 305 at 3:00 pm


Functional Data Analysis for Dynamic Biomedical Imaging


 

Yuan Wang



Department of Mathematics

  Washington State University


Non-invasive biomedical imaging technologies have been widely used in various medical disciplines for interrogation of the pathophysiology and pathogenesis of diseases. Functional data are often generated when diseases features are accessed repeatedly over time and at multiple spatially interdependent units. The problem is motivated by a liver cancer study where patients underwent a dynamic computed tomography (CT) protocol to enable evaluation of multiple perfusion characteristics. The study was undertaken with the objective of determining the effectiveness of using perfusion characteristics to identify and discriminate between regions of liver that contain malignant tissues from normal liver tissue. To reduce model complexity and simplify the resulting inference, possible spatial correlation among neighboring units is often neglected. In this work, we consider a multivariate functional data model and propose a modified kernel smoothing estimation to leverage the spatial and temporal correlation. We also address the companion problem of developing a simultaneous classification method that that utilizes the inter-unit correlation information to predict disease state. The proposed method outperforms conventional functional data classification approaches in the presence of strong correlation. The method offers maximal relative improvement in the presence of temporal sparsity wherein measurements are obtainable at only a few time points.