2026-09-20T03:58:15Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/668202022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
H. Sebastian Seung.
Zlateski, Aleksandar
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2011-11-01T19:48:47Z
2011-11-01T19:48:47Z
2011
2011
http://hdl.handle.net/1721.1/66820
757176741
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 43).
In this thesis, I designed and implemented an efficient, parallel, generalized watershed algorithm for hierarchical segmentation of affinity graphs. By introducing four variable parameters the algorithm enables us to use previous knowledge about the input graph in order to achieve better results. The algorithm is very suitable for hierarchical segmentintation of large scale 3D images of the brain tissue obtained by electron microscopy making it an essential tool for reconstructing the brain's neural-networks called connectomes. The algorithm was fully implemented in C++ and tested on a currently largest available affinity graph of size 90GB on which no existent watershed implementation could be applied.
by Aleksandar Zlateski.
M.Eng.
43 p.
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
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reproduction or distribution in any format is prohibited without written
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http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
A design and implementation of an efficient, parallel watershed algorithm for affinity graphs
Efficient watershed algorithm implementation for large affinity graphs
Thesis
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