Multiscale skeletons by image foresting transform and its application to neuromorphometry

The image foresting transform (IFT) reduces optimal image partition problems based on seed pixels to a shortest-path forest problem in a graph, whose solution can be obtained in linear time. Such a strategy has allowed a unified and efficient approach to the design of image processing operators, suc...

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Bibliographic Details
Published inPattern recognition Vol. 35; no. 7; pp. 1571 - 1582
Main Authors Falcão, A.X., da Fontoura Costa, L., da Cunha, B.S.
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.07.2002
Subjects
Euclidean distance transform
Exact dilations
Graph algorithms
Image analysis
Image foresting transform
Label propagation
Multiscale skeletons
Neuromorphometry
Shape filtering
Shape filtering
Image analysis
Exact dilations
Neuromorphometry
Image foresting transform
Graph algorithms
Multiscale skeletons
Label propagation
Euclidean distance transform
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Summary:The image foresting transform (IFT) reduces optimal image partition problems based on seed pixels to a shortest-path forest problem in a graph, whose solution can be obtained in linear time. Such a strategy has allowed a unified and efficient approach to the design of image processing operators, such as edge tracking, region growing, watershed transforms, distance transforms, and connected filters. This paper presents a fast and simple method based on the IFT to compute multiscale skeletons and shape reconstructions without border shifting. The method also generates one-pixel-wide connected skeletons and the skeleton by influence zones, simultaneously, for objects of arbitrary topologies. The results of the work are illustrated with respect to skeleton quality, execution time, and its application to neuromorphometry.
ISSN:0031-3203
1873-5142
DOI:10.1016/S0031-3203(01)00148-0