15,464 research outputs found
SEGMENT3D: A Web-based Application for Collaborative Segmentation of 3D images used in the Shoot Apical Meristem
The quantitative analysis of 3D confocal microscopy images of the shoot
apical meristem helps understanding the growth process of some plants. Cell
segmentation in these images is crucial for computational plant analysis and
many automated methods have been proposed. However, variations in signal
intensity across the image mitigate the effectiveness of those approaches with
no easy way for user correction. We propose a web-based collaborative 3D image
segmentation application, SEGMENT3D, to leverage automatic segmentation
results. The image is divided into 3D tiles that can be either segmented
interactively from scratch or corrected from a pre-existing segmentation.
Individual segmentation results per tile are then automatically merged via
consensus analysis and then stitched to complete the segmentation for the
entire image stack. SEGMENT3D is a comprehensive application that can be
applied to other 3D imaging modalities and general objects. It also provides an
easy way to create supervised data to advance segmentation using machine
learning models
The Digital Anatomist Information System and Its Use in the Generation and Delivery of Web-Based Anatomy Atlases
Advances in network and imaging technology, coupled with the availability of 3-D datasets
such as the Visible Human, provide a unique opportunity for developing information systems
in anatomy that can deliver relevant knowledge directly to the clinician, researcher or educator. A software framework is described for developing such a system within a distributed architecture that includes spatial and symbolic anatomy information resources, Web and custom servers, and authoring and end-user client programs. The authoring tools have been used to create 3-D atlases of the brain, knee and thorax that are used both locally and throughout the world. For the one and a half year period from June 1995–January 1997, the on-line atlases were accessed by over 33,000 sites from 94 countries, with an average of over 4000 ‘‘hits’’ per day, and 25,000 hits per day during peak exam periods. The atlases have been linked to by over 500 sites, and have received at least six unsolicited awards by outside rating institutions. The flexibility of the software framework has allowed the information system to evolve with advances in technology and representation methods. Possible new features include knowledge-based image retrieval and tutoring, dynamic generation of 3-D scenes, and eventually, real-time virtual reality navigation through the body. Such features, when coupled with other on-line biomedical information resources, should lead to interesting new ways for
managing and accessing structural information in medicine
Leading Undergraduate Students to Big Data Generation
People are facing a flood of data today. Data are being collected at
unprecedented scale in many areas, such as networking, image processing,
virtualization, scientific computation, and algorithms. The huge data nowadays
are called Big Data. Big data is an all encompassing term for any collection of
data sets so large and complex that it becomes difficult to process them using
traditional data processing applications. In this article, the authors present
a unique way which uses network simulator and tools of image processing to
train students abilities to learn, analyze, manipulate, and apply Big Data.
Thus they develop students handson abilities on Big Data and their critical
thinking abilities. The authors used novel image based rendering algorithm with
user intervention to generate realistic 3D virtual world. The learning outcomes
are significant
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