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Statements

Subject Item
n2:RIV%2F67985556%3A_____%2F14%3A00438902%21RIV15-TA0-67985556
rdf:type
skos:Concept n16:Vysledek
dcterms:description
For image segmentation in the bioimaging field, the Otsu thresholding algorithm is very often the algorithm of choice. It's simple and fast algorithm. The drwaback of this algorithm is that it does not account for the image contents, and, in the bioimaging context, it often sets the threshold too high. In result, the contours of the resulting binary objects do not fully cover the original objects. An alternative option is represented by algorithms based on iterative optimization, such as the active contours, deformable models, etc. These algorithms are iterative and possibly rather computationally expensive. An interesting trade-off between the two approaches has been described in Snell et al: Segmentation and shape classification of nuclei in DAPI images. This method uses cost function that relates to the quality of resulting boundary. In the poster, an implementation of the Snell algorithm in the form of an ImageJ plugin was presented. For image segmentation in the bioimaging field, the Otsu thresholding algorithm is very often the algorithm of choice. It's simple and fast algorithm. The drwaback of this algorithm is that it does not account for the image contents, and, in the bioimaging context, it often sets the threshold too high. In result, the contours of the resulting binary objects do not fully cover the original objects. An alternative option is represented by algorithms based on iterative optimization, such as the active contours, deformable models, etc. These algorithms are iterative and possibly rather computationally expensive. An interesting trade-off between the two approaches has been described in Snell et al: Segmentation and shape classification of nuclei in DAPI images. This method uses cost function that relates to the quality of resulting boundary. In the poster, an implementation of the Snell algorithm in the form of an ImageJ plugin was presented.
dcterms:title
ImageJ plugin for the Snell segmentation method ImageJ plugin for the Snell segmentation method
skos:prefLabel
ImageJ plugin for the Snell segmentation method ImageJ plugin for the Snell segmentation method
skos:notation
RIV/67985556:_____/14:00438902!RIV15-TA0-67985556
n5:aktivita
n11:I n11:P
n5:aktivity
I, P(TA01010931)
n5:dodaniDat
n8:2015
n5:domaciTvurceVysledku
n12:2365030
n5:druhVysledku
n7:O
n5:duvernostUdaju
n9:S
n5:entitaPredkladatele
n15:predkladatel
n5:idSjednocenehoVysledku
20476
n5:idVysledku
RIV/67985556:_____/14:00438902
n5:jazykVysledku
n6:eng
n5:klicovaSlova
Image Processing; Image Segmentation; ImageJ plugin
n5:klicoveSlovo
n14:Image%20Processing n14:ImageJ%20plugin n14:Image%20Segmentation
n5:kontrolniKodProRIV
[D9AD319F57C9]
n5:obor
n13:JC
n5:pocetDomacichTvurcuVysledku
1
n5:pocetTvurcuVysledku
1
n5:projekt
n10:TA01010931
n5:rokUplatneniVysledku
n8:2014
n5:tvurceVysledku
Schier, Jan