1. Model-Based Strategies for Biomedical Image Analysis James S. Duncan Image Processing and Analysis Group Departments of Biomedical Engineering, Diagnostic Radiology and Electrical Engineering Yale University

2. Experiment – Functional Subnetworks Subgroup probability averaged over ROI Subject 1Subject 2Subject 3Subject 4Subject 5 SNet 1 SNet2SNet1SNet2SNet1SNet2SNet1SNet2SNet1SNet2 AMY0.7930.20.7810.2010.7710.2170.9900.4940.003 FFG0.79400.7770.010.9820.010.78800.3980.004 STS00.7860.0060.7880.2350.7520.20.7850.0030.967 IFG00.8680.0780.7810.0070.78200.980.1010.782 Performed classification on sample of 5 normal child subjects: –Look at average probability within atlas ROI and subgroup of interest –In agreement with proposed subgroups of the “social brain” pink red green purp Green = Amygdala Purple = FFG Red= STS Pink=IFG

3. The Tracking Algorithm (L.Liang, et al., MICCAI 2011) Particle Detection Trajectory Estimation Image Sequences (in Selected Regions) Select ( manually ) regions away from Golgi apparatus and nucleus (1) Find local maxima LoG filter, histogram thresholding (2) Fit Gaussian models ( point spread func. ) Establish the links among detected particles using a multiple hypothesis based method t Anchored Brownian Motion

4. Strain from MRI (Shape-Tracking: Sinusas, et al, AJP, 2003) Normal Canine Heart 1 Hour Post- LAD Occlusion Infarct region strains for N=6 dogs