model-based strategies for biomedical image analysis james s. duncan image processing and analysis...
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Model-Based Strategies for Biomedical Image Analysis
James S. Duncan
Image Processing and Analysis GroupDepartments of Biomedical Engineering, Diagnostic
Radiology and Electrical Engineering
Yale University
Experiment – Functional Subnetworks
Subgroup probability averaged over ROI
Subject 1 Subject 2 Subject 3 Subject 4 Subject 5
SNet1 SNet2 SNet1 SNet2 SNet1 SNet2 SNet1 SNet2 SNet1 SNet2
AMY 0.793 0.2 0.781 0.201 0.771 0.217 0.99 0 0.494 0.003
FFG 0.794 0 0.777 0.01 0.982 0.01 0.788 0 0.398 0.004
STS 0 0.786 0.006 0.788 0.235 0.752 0.2 0.785 0.003 0.967
IFG 0 0.868 0.078 0.781 0.007 0.782 0 0.98 0.101 0.782
• 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= STSPink=IFG
The Tracking Algorithm The Tracking Algorithm (L.Liang, et al., MICCAI 2011)(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.)
2 22
,
, , ,
exp 2
" " | X
k k kx y
k
t tx y x y x y
F f x x y y
I F b Noise p I
max max max1: max
max max
max max
11: 1: 1:
1: 1: 1 12 1
ˆ arg max | , ,..., ,... the joint state matrix of all particles
| | |t
it t t t t t
t tt t t t t tt t
p I X X
p I p p p I
X
X X X
X X X X X
max
12 log X | X
tt tt
Total Cost p
Establish the links among detected particlesusing a multiple hypothesis based method
t
Anchored Brownian Motion
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