`%0 Conference Proceedings`

`%4 sid.inpe.br/sibgrapi/2013/07.12.20.08`

`%2 sid.inpe.br/sibgrapi/2013/07.12.20.08.40`

`%T Image Segmentation by Image Foresting Transform with Non-smooth Connectivity Functions`

`%D 2013`

`%A Mansilla, Lucy A. C.,`

`%A Cappabianco, Fábio A. M.,`

`%A Miranda, Paulo A. V.,`

`%@affiliation Department of Computer Science, University of São Paulo (USP)`

`%@affiliation Instituto de Ciência e Tecnologia, Universidade Federal de São Paulo`

`%@affiliation Department of Computer Science, University of São Paulo (USP)`

`%E Boyer, Kim,`

`%E Hirata, Nina,`

`%E Nedel, Luciana,`

`%E Silva, Claudio,`

`%B Conference on Graphics, Patterns and Images, 26 (SIBGRAPI)`

`%C Arequipa, Peru`

`%8 Aug. 5-8, 2013`

`%I IEEE Computer Society`

`%J Los Alamitos`

`%S Proceedings`

`%K graph search algorithms, image foresting transform, non-smooth connectivity functions.`

`%X In 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.`

`%@language en`

`%3 114732_new.pdf`