Segmentation Methods For Automatic Kidney Volume Quantification In Spect

Boire, Jean-Yves; Cauvin, J.C.; Cluzel, Philippe; Lahellec, M; Maublant, J.; Zanca, Michel; Veyre, A.

doi:10.1109/iembs.1990.691144

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Cited by 4 publications

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“…-original images are segmented by using an auto-adaptive threshold [6]. The binary object obtained with this segmentation represents the normal areas of the myocardium.…”

Section: Principlementioning

confidence: 99%

Application of skeletonization algorithms for myocardial spect quantification

Scellier¹,

Boire²,

Thouly

et al. 1996

Discrete Geometry for Computer Imagery

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Defect quantification is an important topic in myocardial SPECT. It can be achieved properly only with an automatic and reproducible processing. We propose a new approach lying on the application of discrete geometry and of two aspects of mathematical morphology, namely, skeletonlzation and segmentation. The properties of the skeleton are essential for carrying out segmentation, which uses many sights of uncertain's theory (fuzzy logic). We present the basis of this application with an in-depth analysis of various methods of skeletonization.

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“…-original images are segmented by using an auto-adaptive threshold [6]. The binary object obtained with this segmentation represents the normal areas of the myocardium.…”

Section: Principlementioning

confidence: 99%