Quantitative Analysis and Objective Comparison of Clustering Algorithms for Medical Image Segmentation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F20%3A10244962" target="_blank" >RIV/61989100:27240/20:10244962 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-030-42058-1_10" target="_blank" >http://dx.doi.org/10.1007/978-3-030-42058-1_10</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-42058-1_10" target="_blank" >10.1007/978-3-030-42058-1_10</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Quantitative Analysis and Objective Comparison of Clustering Algorithms for Medical Image Segmentation

Popis výsledku v původním jazyce

The paper describes the implementation of non-hierarchical methods k-means and fuzzy c-means on nosily images from different medical modalities as computed tomography and magnetic resonance. Modern devices are created on the basis of advanced technology, both during the actual acquisition of the image and subsequently during its processing. The problem is caused by the unexpected disturbance of the image by parasitic noise, which may already occur in the electronics of the device or in dependence on the phenomena caused by the external environment. The testing was carried out on 3 datasets of medical images and the evaluation per individual images was determined based on the correlation factor and the mean quadratic error. The result is evaluation of non-hierarchical clustering techniques for the creation of mathematical models of tissue depending on the noise intensity. (C) 2020, Springer Nature Switzerland AG.

Název v anglickém jazyce

Quantitative Analysis and Objective Comparison of Clustering Algorithms for Medical Image Segmentation

Popis výsledku anglicky

The paper describes the implementation of non-hierarchical methods k-means and fuzzy c-means on nosily images from different medical modalities as computed tomography and magnetic resonance. Modern devices are created on the basis of advanced technology, both during the actual acquisition of the image and subsequently during its processing. The problem is caused by the unexpected disturbance of the image by parasitic noise, which may already occur in the electronics of the device or in dependence on the phenomena caused by the external environment. The testing was carried out on 3 datasets of medical images and the evaluation per individual images was determined based on the correlation factor and the mean quadratic error. The result is evaluation of non-hierarchical clustering techniques for the creation of mathematical models of tissue depending on the noise intensity. (C) 2020, Springer Nature Switzerland AG.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Volume 12034

ISBN

978-3-030-42057-4

ISSN

0302-9743

e-ISSN

—

Počet stran výsledku

12

Strana od-do

114-125

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Phuket

Datum konání akce

23. 3. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—