Paper Info
Title
Direct Estimation of Biological Growth Properties from Image Data Using the "GRID" Model
Abstract
Having acquired images of a growing organism, the question arises how they can be used to infer the properties of growth. We address this challenging image understanding problem by using the GRID (Growth as Random Iterated Diffeomorphisms) model for biological growth. In the GRID model, growth patterns are composed of smaller, local deformations, each resulting from elementary biological events (e.g.,cell division). A large number of such biological events, each occurring randomly and independently from one another, results in a visible growth pattern or biological shape changes as seen in images. A biological transformation underlying observed shape changes is a solution to a GRID visible growth differential equation. We propose its automatic generation via direct estimation of the growth magnitudes from image data. The growth magnitude is a GRID parameter that characterizes a local expansion/contraction rate throughout the organism's domain. The estimation algorithm is based on the unconstrained optimal control problem formulation expressed in Darcyan coordinates of the organism's domain and consequent application of Polak-Ribiere minimization routine. We demonstrate the proposed inference method using confocal micrographs of the Drosophila wing disc at larval stage of development.
Year
DOI
Venue
2009
10.1007/978-3-642-02611-9_82
ICIAR
Keywords
Field
DocType
biological event,grid visible growth differential,visible growth pattern,biological shape change,elementary biological event,image data,biological growth,growth pattern,direct estimation,grid model,biological growth properties,growth magnitude,biological transformation,differential equation,cell division
Differential equation,Computer vision,Magnitude (mathematics),Wing,Optimal control,Inference,Computer science,Minification,Artificial intelligence,Iterated function,Grid
Conference
Volume
ISSN
Citations 
5627
0302-9743
2
PageRank 
References 
Authors
0.38
8
3
Name
Order
Citations
PageRank
N. Portman131.44
Ulf Grenander230880.59
Edward R. Vrscay323525.15