Elastic matching of very large digital images on high performance clusters

J. Modersitzki; W. Obelöer; O. Schmitt; G. Lustig

doi:10.1007/BFb0100575

Elastic matching of very large digital images on high performance clusters

J. Modersitzki, W. Obelöer, O. Schmitt, G. Lustig

Institute of Mathematics and Image Computing

Abstract

The aim of the human neuroscanning project is to build an atlas of the human brain, based on a variety of image modalities in particular histological sections of a prepared brain. Reconstructing essential information out of deformed images is a key problem. We describe a method to correct elastic deformations. Since the method is computational expensive a parallel implementation is presented. The measurements and results shown are performed on a cluster of 48 Pentium II PCs connected via Myrinet.

Original language	English
Title of host publication	High-Performance Computing and Networking
Editors	Peter Sloot, Marian Bubak, Alfons Hoekstra, Bob Hertzberger
Number of pages	9
Volume	1593
Place of Publication	Berlin, Heidelberg
Publisher	Springer Berlin Heidelberg
Publication date	01.04.1999
Pages	141-149
ISBN (Print)	978-3-540-65821-4
ISBN (Electronic)	978-3-540-48933-7
DOIs	https://doi.org/10.1007/BFb0100575
Publication status	Published - 01.04.1999
Event	7th International Conference on High-Performance Computing and Networking - Amsterdam, Netherlands Duration: 12.04.1999 → 14.04.1999 Conference number: 150469

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Modersitzki, J., Obelöer, W., Schmitt, O., & Lustig, G. (1999). Elastic matching of very large digital images on high performance clusters. In P. Sloot, M. Bubak, A. Hoekstra, & B. Hertzberger (Eds.), High-Performance Computing and Networking (Vol. 1593, pp. 141-149). (Lecture Notes in Computer Science; Vol. 1593). Springer Berlin Heidelberg. https://doi.org/10.1007/BFb0100575

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title = "Elastic matching of very large digital images on high performance clusters",

abstract = "The aim of the human neuroscanning project is to build an atlas of the human brain, based on a variety of image modalities in particular histological sections of a prepared brain. Reconstructing essential information out of deformed images is a key problem. We describe a method to correct elastic deformations. Since the method is computational expensive a parallel implementation is presented. The measurements and results shown are performed on a cluster of 48 Pentium II PCs connected via Myrinet.",

author = "J. Modersitzki and W. Obel{\"o}er and O. Schmitt and G. Lustig",

year = "1999",

month = apr,

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Modersitzki, J, Obelöer, W, Schmitt, O & Lustig, G 1999, Elastic matching of very large digital images on high performance clusters. in P Sloot, M Bubak, A Hoekstra & B Hertzberger (eds), High-Performance Computing and Networking. vol. 1593, Lecture Notes in Computer Science, vol. 1593, Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 141-149, 7th International Conference on High-Performance Computing and Networking, Amsterdam, Netherlands, 12.04.99. https://doi.org/10.1007/BFb0100575

Elastic matching of very large digital images on high performance clusters. / Modersitzki, J.; Obelöer, W.; Schmitt, O. et al.

High-Performance Computing and Networking. ed. / Peter Sloot; Marian Bubak; Alfons Hoekstra; Bob Hertzberger. Vol. 1593 Berlin, Heidelberg : Springer Berlin Heidelberg, 1999. p. 141-149 (Lecture Notes in Computer Science; Vol. 1593).

Research output: Chapters in Books/Reports/Conference Proceedings › Conference contribution › peer-review

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AB - The aim of the human neuroscanning project is to build an atlas of the human brain, based on a variety of image modalities in particular histological sections of a prepared brain. Reconstructing essential information out of deformed images is a key problem. We describe a method to correct elastic deformations. Since the method is computational expensive a parallel implementation is presented. The measurements and results shown are performed on a cluster of 48 Pentium II PCs connected via Myrinet.

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Y2 - 12 April 1999 through 14 April 1999

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Elastic matching of very large digital images on high performance clusters

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