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Reference TypeConference Paper (Conference Proceedings)
Last Update2013:
Metadata Last Update2020: administrator
Citation KeyMansillaCappMira:2013:ImSeIm
TitleImage Segmentation by Image Foresting Transform with Non-smooth Connectivity Functions
Access Date2021, Jan. 27
Number of Files1
Size603 KiB
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Author1 Mansilla, Lucy A. C.
2 Cappabianco, Fábio A. M.
3 Miranda, Paulo A. V.
Affiliation1 Department of Computer Science, University of São Paulo (USP)
2 Instituto de Ciência e Tecnologia, Universidade Federal de São Paulo
3 Department of Computer Science, University of São Paulo (USP)
EditorBoyer, Kim
Hirata, Nina
Nedel, Luciana
Silva, Claudio
Conference NameConference on Graphics, Patterns and Images, 26 (SIBGRAPI)
Conference LocationArequipa, Peru
DateAug. 5-8, 2013
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Tertiary TypeFull Paper
History2013-07-12 20:08:40 :: -> administrator ::
2020-02-19 03:09:22 :: administrator -> :: 2013
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Is the master or a copy?is the master
Content Stagecompleted
Content TypeExternal Contribution
Keywordsgraph search algorithms, image foresting transform, non-smooth connectivity functions.
AbstractIn the framework of the Image Foresting Transform (IFT), there is a class of connectivity functions that were vaguely explored, which corresponds to the non-smooth connectivity functions (NSCF). These functions are more adaptive to cope with the problems of field inhomogeneity, which are common in MR images of 3 Tesla. In this work, we investigate the NSCF from the standpoint of theoretical and experimental aspects. We formally classify several non-smooth functions according to a proposed diagram representation. Then, we investigate some theoretical properties for some specific regions of the diagram. Our analysis reveals that many NSCFs are, in fact, the result of a sequence of optimizations, each of them involving a maximal set of elements, in a well-structured way. Our experimental results indicate that substantial improvements can be obtained by NSCFs in the 3D segmentation of MR images of 3 Tesla, when compared to smooth connectivity functions.
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