Effectivity of Spherical Object Reconstruction Using Star-Shaped Simplex Meshes

MATULA, Pavel. Effectivity of Spherical Object Reconstruction Using Star-Shaped Simplex Meshes. In 1st International Symposium on 3D Data Processing Visualization and Transmission. Los Alamitos: IEEE Computer Society, 2002, p. 794-799. ISBN 0-7695-7521-5.

Basic information

Original name

Effectivity of Spherical Object Reconstruction Using Star-Shaped Simplex Meshes

Authors

MATULA, Pavel

Edition

Los Alamitos, 1st International Symposium on 3D Data Processing Visualization and Transmission, p. 794-799, 2002

Publisher

IEEE Computer Society

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Other information

Language

English

Type of outcome

Proceedings paper

Field of Study

20200 2.2 Electrical engineering, Electronic engineering, Information engineering

Country of publisher

United States of America

Confidentiality degree

is not subject to a state or trade secret

RIV identification code

RIV/00216224:14330/02:00006085

Organization unit

Faculty of Informatics

ISBN

0-7695-7521-5

UT WoS

000177028700124

Keywords in English

spherical object reconstruction; simplex meshes; deformable models; cell nuclei segmentation

Tags

Cell nuclei segmentation, deformable models, simplex meshes, spherical object reconstruction

Tags

International impact, Reviewed

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Abstract

In the original language

Three-dimensional objects of an arbitrary shape and topology can be reconstructed using Delingette's general reconstruction algorithm based on simplex meshes. The method can handle volumetric images as well as three-dimensional range data. The reconstruction is performed in two stages. First, the initialization stage creates a simplex mesh in the vicinity of the input data. Then, an iterative refinement algorithm decreases the distance of the mesh from the data while preserving required shape qualities of the mesh. The general method was adapted to the reconstruction of spherical objects. The basic idea is to consider only star-shaped simplex meshes for the representation of object boundaries and therefore the iterative refinement algorithm is faster and more stable. This paper provides comparison of the effectivity of both methods. The methods were tested on real volumetric images of cell nuclei which were acquired using an optical microscope.

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Links

IAB5004102, research and development project	Name: Jaderná topologie některých protoonkogenů u lidských neutrofilních granulocytů a leukemických buněk Investor: Academy of Sciences of the Czech Republic, Nuclear topology of some protooncogenes in human neutrophilic granulocytes and leukemic cells
IBS5004010, research and development project	Name: Vývoj nových diagnostických technik pro onkologii Investor: Academy of Sciences of the Czech Republic, Development of new diagnostic teniques for oncology
MSM 143300002, plan (intention)	Name: Využití počítačové analýzy obrazu v optické mikroskopii Investor: Ministry of Education, Youth and Sports of the CR, Application of computer image analysis in optical microscopy