Image Processing Reference
In-Depth Information
Several common themes can be identified in this field. First, to keep up with
the accelerated experimental process in biology, visualization tools should be devel-
oped rapidly to be relevant. A second challenge is the integration of many different
types of data. Visualization should support the discovery of complex patterns in such
heterogeneous data. Thirdly, close collaboration between biologists and visualiza-
tion researchers is essential for requirement elicitation and prototype design. The
chapter discusses visualization in the context of comparative genomics and func-
tional genomics, as well as evolutionary and developmental biology. The common
types of biological data, questions and methods in each of these fields are covered,
along with visualization challenges and case studies that highlight the biological
impact of visualization tools.
In the field of comparative genomics, scientists compare the genomes of organisms
to answer questions about evolution and how the genome encodes cellular functions.
Here, several challenges arise. First, the number of features to be compared can easily
run into the thousands. Second, the size of the features is often orders of magnitude
smaller than the size of the chromosomes. Third, it is often important to understand
the location and size of paired features in the context of their similarity scores. Many
current visualization tools do not support this.
Functional genomics studies howgenes work together in a cell to performdifferent
cellular functions, such as metabolism or reproduction, and how these are controlled
by many interrelated chemical reactions which form complicated networks. Finding
differences and similarities in networks from different experimental conditions, in
different cell types, and in different species is an important component of functional
genomics. Again, scale is a major challenge. The number of nodes and links in the
network can become very large. Major questions are how to visualize such networks
on different scales, how to support interactive exploration and pattern discovery, how
to understand changes over time, and, maybe most difficult of all, how to integrate
the various data.
Finally, in evolutionary and developmental biology, scientists can nowadays cap-
ture data about living organisms at an unprecedented level of detail in time and space.
For example, it is possible to identify each single cell of a complex organism, follow
its development over time, and connect this with genetic information. This allows
the scientist to study how a single cell evolves into a complex organism, how inter-
nal regulatory processes cause differentiation, or how genomic differences relate to
differences in physiological structure. Due to the wealth of data, robust automatic
preprocessing and a sophisticated visualization framework are central requirements
to allow for future advances in this field.
Chapter
23
is about medical visualization. Given the ubiquitous nature of medical
volume data, medical visualization is now an established branch of visualization,
with applications in medical diagnosis, treatment, research and education. During
the past decades, medical image acquisition technology has undergone continuous
and rapid development. It is now possible to acquire larger and more complex data
than ever before by techniques such as computed tomography, (functional) mag-
netic resonance imaging, electroencephalography, diffusion tensor imaging, etc. The
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