Image Processing Reference
In-Depth Information
22.4 Evolutionary and Developmental Biology
A combination of novel microscopes with improved computer hardware and software
has enabled scientists in recent years to capture data about living organisms at an
unprecedented level of detail in time and space. Using this data, biologist can identify
each single cell of a complex organism and investigate how it develops over time.
With additional highlighting procedures, complementary information such as active
genes can be recorded simultaneously to link the acquired information to processes
on even finer biological scales. This wealth of information opens up new roads
to study complex, yet fundamental processes in evolutionary and developmental
biology (informally called evo-devo), such as: How does a single cell evolve into
a complex organism? How do internal regulatory processes cause (nearly) identical
cells to perform different tasks? How do genomic differences relate to differences in
physiological structure?Howdo neurons connect to formrich and powerful structures
within the brain?
Due to the wealth of evo-devo data and the relevant information therein, robust
automatic preprocessing and a sophisticated visualization framework are central
requirements to allow for future advances in this field. The fundamental data process-
ing pipeline consists of three major steps:
1.
Data acquisition and storage:
commonly from digital microscopy
2.
Data preprocessing:
data fusion, image processing, and feature extraction
3.
Visualization and data analysis:
rendering of large-scale spatio-temporal data and
deployment of application-focused interaction and data-mining techniques
In the following sections, we will investigate each step and outline relevant aspects
for data analysis in general and data visualization in particular.
22.4.1 Data Acquisition and Storage
One of the most widely used data acquisition techniques in developmental biol-
ogy is digital microscopy. Numerous specialized microscopes developed over the
last decades support a large variety of applications. Most techniques enable the
user to record 2D and 3D data, either from a single, volumetric specimen, or from
cross-sections of one. Additional techniques exist to record volumetric time-series
of living specimen (laser scanning microscopy) or data with a very high spatial reso-
lution (electron microscopy). A variety of experimental preprocessing steps, such as
selective dyes or fluorescent markers [
19
,
47
], enhance data quality by highlighting
structures and processes of interest. More details on experimental methods can be
found in an excellent summary of microscopy usage in biology [
49
].
A lack of standardization in storage for microscopy data has resulted in a large
variety of file formats, which poses a major challenge for generalizing processing
pipelines. Most data, however, is stored as image files using, for example, the JPEG
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