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231223s2001 xx |||||o 00| ||eng c |
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|a 10.1109/83.931095
|2 doi
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|a eng
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|a Zana, F
|e verfasserin
|4 aut
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|a Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation
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|c 2001
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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|a ƒa Online-Ressource
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|2 rdacarrier
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| 500 |
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|a Date Completed 20.05.2010
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| 500 |
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|a Date Revised 09.03.2022
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|a published: Print
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|a Citation Status PubMed-not-MEDLINE
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|a This paper presents an algorithm based on mathematical morphology and curvature evaluation for the detection of vessel-like patterns in a noisy environment. Such patterns are very common in medical images. Vessel detection is interesting for the computation of parameters related to blood flow. Its tree-like geometry makes it a usable feature for registration between images that can be of a different nature. In order to define vessel-like patterns, segmentation is performed with respect to a precise model. We define a vessel as a bright pattern, piece-wise connected, and locally linear, mathematical morphology is very well adapted to this description, however other patterns fit such a morphological description. In order to differentiate vessels from analogous background patterns, a cross-curvature evaluation is performed. They are separated out as they have a specific Gaussian-like profile whose curvature varies smoothly along the vessel. The detection algorithm that derives directly from this modeling is based on four steps: (1) noise reduction; (2) linear pattern with Gaussian-like profile improvement; (3) cross-curvature evaluation; (4) linear filtering. We present its theoretical background and illustrate it on real images of various natures, then evaluate its robustness and its accuracy with respect to noise
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|a Journal Article
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|a Klein, J C
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 10(2001), 7 vom: 15., Seite 1010-9
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|x 1941-0042
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|g volume:10
|g year:2001
|g number:7
|g day:15
|g pages:1010-9
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|u http://dx.doi.org/10.1109/83.931095
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