Biomedical Image Analysis: Tracking

Overview

Biomedical Image Analysis: Tracking addresses methods for extracting image information from biological/medical images for use in tracking biological targets. Here, and in the forthcoming companion Biomedical Image Analysis: Segmentation (Morgan & Claypool, ISBN: 1598290207), the authors concentrate on aspects of image analysis rather than the modalities or the imaging process itself. This lecture will be a valuable resource for graduate students, faculty, and industrial/governmental researchers interested in ...

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Overview

Biomedical Image Analysis: Tracking addresses methods for extracting image information from biological/medical images for use in tracking biological targets. Here, and in the forthcoming companion Biomedical Image Analysis: Segmentation (Morgan & Claypool, ISBN: 1598290207), the authors concentrate on aspects of image analysis rather than the modalities or the imaging process itself. This lecture will be a valuable resource for graduate students, faculty, and industrial/governmental researchers interested in applications of imaging, or more specifically, biomedical imaging. It is written from first principles and will be accessible to a broad readership.

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Product Details

Table of Contents

Introduction     01
Active Contours for Tracking     05
Overview     5
The Basic Snake Model     6
Snake External Forces     18
The Balloon Force     20
Gradient Vector Flow     22
Case Study: Tracking with Snakes     25
External Force for Cell Tracking Case Study     29
Motion Gradient Vector Flow     30
Computation of Motion Gradient Vector Flow Field     32
Choosing Parameter Values     34
Dynamic Programming for Snake Evolution     38
Conclusions     43
Bayesian Tracking and the Kalman Filter     45
Overview     45
Sequential Bayesian Filtering     46
Kalman Filter     50
Case study: The Alpha-Beta Filter     52
Alpha-Beta Filter Gains     55
Initializing the Kalman Tracker     57
Executing the Alpha-Beta Filter     58
Obtaining Measurements for the Alpha-Beta Filter     58
The Extended Kalman Filter     62
Interacting Multiple Models for Tracking     64
Summary     69
Particle Filters and Multi-Target Tracking     71
Overview     71
The Particle Filter     72
The CONDENSATION Algorithm     74
Auxiliary Particle Filters     75
Multi-Target Tracking     77
Multiple Hypothesis Tracking     78
Joint Probabilistic Data Association     79
Markov Chain Monte Carlo Methods     81
Metropolis-Hastings Sampling     81
Auction Algorithm     85
Case Studies     87
Leukocyte Tracking with CONDENSATION     88
Multiple Cell Tracking with MCMC     93
Summary     98
Tracking Shapes by Sampling     99
Overview     99
Tracking Rigid Shapes     100
Tracking by Affine and Projective Snakes     101
Affine Snakes for Tracking     101
Projective Snakes for Tracking     107
Tracking Deformable Shapes     108
Stochastic Snakes for Tracking: Simulated Annealing and Deterministic Annealing     108
Sequential Bayesian Formulation     120
Summary     125

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