Biomedical Image Analysis: Tracking

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Morgan & Claypool Publishers, 2006 - Medical - 144 pages
In biological and medical imaging applications, tracking objects in motion is a critical task. This book describes the state-of-the-art in biomedical tracking techniques. We begin by detailing methods for tracking using active contours, which have been highly successful in biomedical applications. The book next covers the major probabilistic methods for tracking. Starting with the basic Bayesian model, we describe the Kalman filter and conventional tracking methods that use centroid and correlation measurements for target detection. Innovations such as the extended Kalman filter and the interacting multiple model open the door to capturing complex biological objects in motion. A salient highlight of the book is the introduction of the recently emerged particle filter, which promises to solve tracking problems that were previously intractable by conventional means. Another unique feature of Biomedical Image Analysis: Tracking is the explanation of shape-based methods for biomedical image analysis. Methods for both rigid and nonrigid objects are depicted. Each chapter in the book puts forth biomedical case studies that illustrate the methods in action. In biological and medical imaging applications, tracking objects in motion is a critical task. This book describes the state-of-the-art in biomedical tracking techniques. We begin by detailing methods for tracking using active contours, which have been highly successful in biomedical applications. The book next covers the major probabilistic methods for tracking. Starting with the basic Bayesian model, we describe the Kalman filter and conventional tracking methods that use centroid and correlation measurements for target detection. Innovations such as the extended Kalman filter and the interacting multiple model open the door to capturing complex biological objects in motion.
 

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Contents

Active Contours for Tracking
5
22 THE BASIC SNAKE MODEL
6
23 SNAKE EXTERNAL FORCES
18
231 The Balloon Force
20
232 Gradient Vector Flow
22
TRACKING WITH SNAKES
25
241 External Force for Cell Tracking Case Study
29
242 Motion Gradient Vector Flow
30
Particle Filters and MultiTarget Tracking
71
42 THE PARTICLE FILTER
72
421 The CONDENSATION Algorithm
74
422 Auxiliary Particle Filters
75
43 MULTITARGET TRACKING
77
431 Multiple Hypothesis Tracking
78
432 Joint Probabilistic Data Association
79
433 Markov Chain Monte Carlo Methods
81

243 Computation of Motion Gradient Vector Flow Field
32
25 CHOOSING PARAMETER VALUES
34
26 DYNAMIC PROGRAMMING FOR SNAKE EVOLUTION
38
27 CONCLUSIONS
43
Bayesian Tracking and the Kalman Filter
45
32 SEQUENTIAL BAYESIAN FILTERING
46
33 KALMAN FILTER
50
THE ALPHABETA FILTER
52
341 AlphaBeta Filter Gains
55
342 Initializing the Kalman Tracker
57
343 Executing the AlphaBeta Filter
58
35 THE EXTENDED KALMAN FILTER
62
36 INTERACTING MULTIPLE MODELS FOR TRACKING
64
37 SUMMARY
69
435 Auction Algorithm
85
44 CASE STUDIES
87
441 Leukocyte Tracking with CONDENSATION
88
442 Multiple Cell Tracking with MCMC
93
45 SUMMARY
98
Tracking Shapes by Sampling
99
52 TRACKING RIGID SHAPES
100
521 Tracking by Affine and Projective Snakes
101
523 Projective Snakes for Tracking
107
53 TRACKING DEFORMABLE SHAPES
108
532 Sequential Bayesian Formulation
120
54 SUMMARY
125
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