Circular and oval-like objects are very common in cell and micro biology. These objects need to be analyzed, and to
that end, digitized images from the microscope are used so as to come to an automated analysis pipeline. It is essential
to detect all the objects in an image as well as to extract the exact contour of each individual object. In this manner it
becomes possible to perform measurements on these objects, i.e. shape and texture features. Our measurement objective
is achieved by probing contour detection through dynamic programming. In this paper we describe a method that uses
Hough transform and two minimal path algorithms to detect contours of (ovoid-like) objects. These algorithms are based
on an existing grey-weighted distance transform and a new algorithm to extract the circular shortest path in an image. The
methods are tested on an artificial dataset of a 1000 images, with an F1-score of 0.972. In a case study with yeast cells,
contours from our methods were compared with another solution using Pratt’s figure of merit. Results indicate that our
methods were more precise based on a comparison with a ground-truth dataset. As far as yeast cells are concerned, the
segmentation and measurement results enable, in future work, to retrieve information from different developmental stages
of the cell using complex features.
In computer vision and graphics, reconstruction of a three-dimensional surface from a point cloud is a well-studied research area. As the surface contains information that can be measured, the application of surface reconstruction may be potentially important for applications in bioimaging. In the past decade, a number of algorithms for surface reconstruction have been developed. Generally speaking, these algorithms can be separated into two categories: explicit representation and implicit approximation. Most of these algorithms have a sound basis in mathematical theory. However, so far, no analytical evaluation between these algorithms has been presented. The straightforward method of evaluation has been by convincing through visual inspection. Therefore, we design an analytical approach by selecting surface distance, surface area, and surface curvature as three major surface descriptors. We evaluate these features in varied conditions. Our ground truth values are obtained from analytical shapes: the sphere, the ellipsoid, and the oval. Through evaluation we search for a method that can preserve the surface characteristics best and which is robust in the presence of noise. The results obtained from our experiments indicate that Poisson reconstruction method performs best. This outcome can now be used to produce reliable surface reconstruction of biological models.
In computer graphics and visualization, reconstruction of a 3D surface from a point cloud is an important research area.
As the surface contains information that can be measured, i.e. expressed in features, the application of surface
reconstruction can be potentially important for application in bio-imaging. Opportunities in this application area are the
motivation for this study. In the past decade, a number of algorithms for surface reconstruction have been proposed.
Generally speaking, these methods can be separated into two categories: i.e., explicit representation and implicit
approximation.
Most of the aforementioned methods are firmly based in theory; however, so far, no analytical evaluation between these
methods has been presented. The straightforward way of evaluation has been by convincing through visual inspection.
Through evaluation we search for a method that can precisely preserve the surface characteristics and that is robust in the
presence of noise. The outcome will be used to improve reliability in surface reconstruction of biological models. We,
therefore, use an analytical approach by selecting features as surface descriptors and measure these features in varying
conditions. We selected surface distance, surface area and surface curvature as three major features to compare quality of
the surface created by the different algorithms. Our starting point has been ground truth values obtained from analytical
shapes such as the sphere and the ellipsoid.
In this paper we present four classical surface reconstruction methods from the two categories mentioned above, i.e. the
Power Crust, the Robust Cocone, the Fourier-based method and the Poisson reconstruction method. The results obtained
from our experiments indicate that Poisson reconstruction method performs the best in the presence of noise.
KEYWORDS: 3D modeling, Visualization, Data modeling, Web services, 3D image processing, Visual process modeling, Ions, Visual analytics, Associative arrays, Neodymium
The analysis of patterns of gene expression patterns analysis plays an important role in developmental biology and
molecular genetics. Visualizing both quantitative and spatio-temporal aspects of gene expression patterns together with
referenced anatomical structures of a model-organism in 3D can help identifying how a group of genes are expressed at a
certain location at a particular developmental stage of an organism. In this paper, we present an approach to provide an
online visualization of gene expression data in zebrafish (Danio rerio) within 3D reconstruction model of zebrafish in
different developmental stages. We developed web services that provide programmable access to the 3D reconstruction
data and spatial-temporal gene expression data maintained in our local repositories. To demonstrate this work, we
develop a web application that uses these web services to retrieve data from our local information systems. The web
application also retrieve relevant analysis of microarray gene expression data from an external community resource; i.e.
the ArrayExpress Atlas. All the relevant gene expression patterns data are subsequently integrated with the
reconstruction data of the zebrafish atlas using ontology based mapping. The resulting visualization provides quantitative
and spatial information on patterns of gene expression in a 3D graphical representation of the zebrafish atlas in a certain
developmental stage. To deliver the visualization to the user, we developed a Java based 3D viewer client that can be
integrated in a web interface allowing the user to visualize the integrated information over the Internet.
In this paper we introduce methods for the visualization of ontologies using different geometrical representations. An
ontology is a formal way to define domain knowledge by means of axioms about domain concepts, properties and
individuals. Currently, ontologies are modeled with the OWL language; this language is very expressive and provides
challenges for ontology visualization. Expressive ontologies can be difficult to understand and to that end ontology
visualization can be extremely helpful for ontology inspection during the process of development as well as for
inspection of existing ontologies.
Our improved approach for ontology visualization includes two different tree-visualization techniques: i.e., the node-link
technique and the containment technique. The node-link technique visualizes the ontology as a graph. The graph can be
build for each concept with different levels of depth. The core visualization component is based on the spanning tree
skeleton of the graph and it includes five different geometrical views, i.e., two Euclidean, two hyperbolic and one
spherical. All the views are augmented with corresponding geometrical transformations so that user interaction like pan,
zoom and rotate can be invoked. Another approach encompasses a 3-dimensional spherical alternative of the treemap
method, in which nodes are placed on the surface of a sphere. Each parental node contains its children, which are places
on the surface of the parent. We augmented this method with semantic zoom technique. With this technique the level of
details depends on the distance from the viewer. Our approach provides the means to visualize ontology from different
perspectives and different levels of detail. The interaction that is provided greatly enhances the user perception of
otherwise complex information.
Image collections are most often domain specific. We have developed a system for image retrieval of multimodal
microscopy images. That is, the same object of study visualized with a range of microscope techniques and with a range
of different resolutions. In microscopy, image content is depending on the preparation method of the object under study
as well as the microscope technique. Both are taken into account in the submission phase as metadata whilst at the same
time (domain specific) ontologies are employed as controlled vocabularies to annotate the image. From that point
onward, image data are interrelated through the relationships derived from annotated concepts in the ontology. By using
concepts and relationships of an ontology, complex queries can be built with true semantic content. Image metadata can
be used as powerful criteria to query image data which are directly or indirectly related to original data. The results of
image retrieval can be represented using a structural graph by exploiting relationships from ontology rather than a listed
table. Applying this to retrieve images from the same subject at different levels of resolution opens a new field for the
analysis of image content.
Morphometrics from images, image analysis, may reveal differences between classes of objects present in the images.
We have performed an image-features-based classification for the pathogenic yeast Cryptococcus neoformans. Building
and analyzing image collections from the yeast under different environmental or genetic conditions may help to
diagnose a new "unseen" situation. Diagnosis here means that retrieval of the relevant information from the image
collection is at hand each time a new "sample" is presented. The basidiomycetous yeast Cryptococcus neoformans can
cause infections such as meningitis or pneumonia. The presence of an extra-cellular capsule is known to be related to
virulence. This paper reports on the approach towards developing classifiers for detecting potentially more or less
virulent cells in a sample, i.e. an image, by using a range of features derived from the shape or density distribution. The
classifier can henceforth be used for automating screening and annotating existing image collections. In addition we will
present our methods for creating samples, collecting images, image preprocessing, identifying "yeast cells" and creating
feature extraction from the images. We compare various expertise based and fully automated methods of feature
selection and benchmark a range of classification algorithms and illustrate successful application to this particular
domain.
In scientific communities images play a dominant role to convey a message but more important, as a tool for experimental output. The meaning of these images develops from annotation that is provided by the researchers. Annotation can be accomplished in a number of ways. In this paper we describe graphical and textual annotations that are developed from ontologies. The Internet has provided the research community a medium for exchange of images. Images are, however, not straightforwardly suitable for exchange. Knowledge about what is depicted in the image as well as specific image content is important for image understanding. This holds in particular for scientific images that are the result of experimentation. For the purpose of image exchange, that is query-based search, image retrieval mechanisms based on pixel content as well as semantics are developed. In the field of experimental imaging new paradigms will have to be developed so that a search query results in correct image collections.
This paper discusses the application of the Active Shape Model as a tool to compute the orientation of a specimen that has been subjected to CLSM imaging. The images have captured patterns of gene expression and these will have to be submitted to and stored in a database. Having all of these patterns available will enable spatio-temporal mining of image data and other linked data. The applications discussed in this paper focus on the zebrafish model system. The gene expression database employs a 3D digital atlas as a reference system in combination with an Active Shape Model. The Active Shape Model is applied with a few anatomical structures and will be scaled up to be usable with more structures in the future.
This paper describes a set-up for high-resolution imaging with a conventional microscope that allows producing a 3D-image set from which a 3D model can be derived. The 3D-image set, is de facto, a gray value voxel model. In order to obtain such 3D image set a good administration needs to be maintained at acquisition and moreover, the acquisition must be realized in two steps. High-resolution images are built of image tiles and sophisticated algorithms are required to build a coherent image from the tiles.
KEYWORDS: 3D modeling, Visualization, Internet, Databases, Data modeling, 3D image processing, Java, 3D visualizations, Systems modeling, Image resolution
We have designed and implemented a 3D digital atlas of zebrafish development. In this paper we focus on the presentation of the data of this atlas using the Internet. An important part of the data of this atlas is graphical. Therefore special effort is put into fast and comprehensive visualization of the 2D and 3D graphical data, i.e. images and their annotations as well as the 3D models that arise from the application of the annotation process on a coherent 3D dataset. These data are presented to the end-user and the modes of visualization need be linked to provide the user with feedback. The success of visualization over the Internet is partially depending on the transfer time of the data as well as the computational load of the data at the client side. The applications discussed in this paper are optimized for transfer time and computational load.
KEYWORDS: Databases, 3D modeling, Data modeling, Standards development, Image processing, 3D image processing, Image segmentation, Systems modeling, Associative arrays, Data acquisition
In developmental biology an overwhelming amount of experimental data concerning patterns of gene expression is produced revealing the genetic layout of the embryo and finding evidence for anomalies. Genes are part of complex genetic cascades and consequently their study requires tools for handling combinatorial problems. Gene expression is spatio-temporal and generally, imagin is used to analyze expression in four dimensions. Reporting and retrieving experimental data has become so complex that printed literature is no longer adequate and therefore databases are being implemented. Zebrafish is a popular model system in developmental biology. We are developing a 3D digital atlas of the zebrafish embryo, which is envisaged as standard allowing comparisons of experimentally induced and normally developing embryos. This 3D atlas is based on microscopical anatomy. From serial sections 3D images are reconstructed by capturing section images and registering these images respectively. This is accomplished for al developmental stages. Data management is solved using XML which is platform independent, ASCII-based, interchangeable and allows easy browsing. Applying supervised segmentation accomplishes a completely anatomically annotated 3D image. It divides the image into domains required for comparison and mapping. Experts provided with dedicated software and Internet-access to the images review annotations. Complete annotation and review is stored in a database.
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