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

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exp 2

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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

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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

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p I X X

p I p p p I

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X X X

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12 log X | X

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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