Modeling of Articular Cartilage with Goal of Early Osteoarthritis Extraction Based on Local Fuzzy Thresholding Driven by Fuzzy C-Means Clustering

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F19%3A10242730" target="_blank" >RIV/61989100:27240/19:10242730 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-3-030-14802-7_25" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-030-14802-7_25</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-14802-7_25" target="_blank" >10.1007/978-3-030-14802-7_25</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Modeling of Articular Cartilage with Goal of Early Osteoarthritis Extraction Based on Local Fuzzy Thresholding Driven by Fuzzy C-Means Clustering

Popis výsledku v původním jazyce

One of the routine tasks in the Orthopedics practice is the articular cartilage assessment. Proper cartilage assessment includes a precise localization, and recognition of spots indicating the cartilage loss caused by the osteoarthritis. Unfortunately, such tasks are performed manually, without the SW feedback, which leads to various clinical outputs based on the physician&apos;s experience. Based on such facts, a development of the fully automatic systems bringing automatic modeling and classification of the cartilage is clinically very important. In our paper we have proposed a local thresholding multiregional segmentation method for the cartilage segmentation from the MR (Magnetic Resonance) images. In our approach, an optimal configuration of the fuzzy triangular sets is driven by the FCM clustering to obtain an optimal segmentation model based on the thresholding. We have verified the proposed model on a sample of the 200 MR image records containing the early osteoarthritis signs. (C) 2019, Springer Nature Switzerland AG.

Název v anglickém jazyce

Modeling of Articular Cartilage with Goal of Early Osteoarthritis Extraction Based on Local Fuzzy Thresholding Driven by Fuzzy C-Means Clustering

Popis výsledku anglicky

One of the routine tasks in the Orthopedics practice is the articular cartilage assessment. Proper cartilage assessment includes a precise localization, and recognition of spots indicating the cartilage loss caused by the osteoarthritis. Unfortunately, such tasks are performed manually, without the SW feedback, which leads to various clinical outputs based on the physician&apos;s experience. Based on such facts, a development of the fully automatic systems bringing automatic modeling and classification of the cartilage is clinically very important. In our paper we have proposed a local thresholding multiregional segmentation method for the cartilage segmentation from the MR (Magnetic Resonance) images. In our approach, an optimal configuration of the fuzzy triangular sets is driven by the FCM clustering to obtain an optimal segmentation model based on the thresholding. We have verified the proposed model on a sample of the 200 MR image records containing the early osteoarthritis signs. (C) 2019, Springer Nature Switzerland AG.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

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

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2019

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 11432

ISBN

978-3-030-14801-0

ISSN

0302-9743

e-ISSN

1611-3349

Počet stran výsledku

11

Strana od-do

289-299

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Jogdžakarta

Datum konání akce

8. 4. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000493319700024