Interactive and intuitive segmentation of volumetric data: the segmentVIEW system and the Kooshball algorithm

Dobrzeniecki, A.B.; Levitt, Nicholas D.

doi:10.1109/icip.1995.537691

Cited by 2 publications

(1 citation statement)

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“…The seed contour and the reference contours are drawn (using special sectioning and visualization tools [31]) on parallel sections, which can arbitrarily cut the 3D data set. Fig.…”

Section: Specification Of Contoursmentioning

confidence: 99%

Genetic algorithm-based interactive segmentation of 3D medical images

Cagnoni

Dobrzeniecki

Poli

et al. 1999

Image and Vision Computing

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102

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This article describes a method for evolving adaptive procedures for the contour-based segmentation of anatomical structures in 3D medical data sets. With this method, the user first manually traces one or more 2D contours of an anatomical structure of interest on parallel planes arbitrarily cutting the data set. Such contours are then used as training examples for a genetic algorithm to evolve a contour detector. By applying the detector to the rest of the image sequence it is possible to obtain a full segmentation of the structure. The same detector can then be used to segment other image sequences of the same sort. Segmentation is driven by a contour-tracking strategy that relies on an elastic-contour model whose parameters are also optimized by the genetic algorithm. We report results obtained on a software-generated phantom and on real tomographic images of different sorts. ᭧

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“…The seed contour and the reference contours are drawn (using special sectioning and visualization tools [31]) on parallel sections, which can arbitrarily cut the 3D data set. Fig.…”

Section: Specification Of Contoursmentioning

confidence: 99%