Fuzzy Segmentation Driven by Modified ABC Algorithm Using Cartilage Features Completed by Spatial Aggregation: Modeling of Early Cartilage Loss

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F18%3A10241594" target="_blank" >RIV/61989100:27240/18:10241594 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-319-98446-9_45" target="_blank" >http://dx.doi.org/10.1007/978-3-319-98446-9_45</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-98446-9_45" target="_blank" >10.1007/978-3-319-98446-9_45</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Fuzzy Segmentation Driven by Modified ABC Algorithm Using Cartilage Features Completed by Spatial Aggregation: Modeling of Early Cartilage Loss

Popis výsledku v původním jazyce

In a clinical practice of the orthopedics, the articular cartilage assessment is one of the major clinical procedures serving as a predictor of the future cartilage loss development. The early stage of the cartilage osteoarthritis is badly observable from the native MR records due to weak contrast between the physiological cartilage and the osteoarthritic spots. Therefore, the cartilage regional modeling would reliably differentiate the physiological cartilage from the early cartilage deterioration, and can serve as an effective clinical tool. In a comparison with the conventional segmentation methods based on the hard thresholding, the soft fuzzy thresholding based on the histogram separation into segmentation classes via the fuzzy triangular functions represents a sensitive regional segmentation even in the non-contrast environment. We have proposed the soft segmentation where the fuzzy sets are driven by the ABC genetic algorithm to optimal fuzzy class&apos;s distribution regarding the knee tissues characteristics. Consequently, the spatial aggregation is employed to taking advantage the spatial dependences which allows for modification the original fuzzy membership function. This procedure ensures the correct pixel&apos;s classification especially when the noise pixels are present. Such multiregional segmentation makes a mathematical model well separating the physiological cartilage from the early osteoarthritic spots which are highlighted in the model.

Název v anglickém jazyce

Fuzzy Segmentation Driven by Modified ABC Algorithm Using Cartilage Features Completed by Spatial Aggregation: Modeling of Early Cartilage Loss

Popis výsledku anglicky

In a clinical practice of the orthopedics, the articular cartilage assessment is one of the major clinical procedures serving as a predictor of the future cartilage loss development. The early stage of the cartilage osteoarthritis is badly observable from the native MR records due to weak contrast between the physiological cartilage and the osteoarthritic spots. Therefore, the cartilage regional modeling would reliably differentiate the physiological cartilage from the early cartilage deterioration, and can serve as an effective clinical tool. In a comparison with the conventional segmentation methods based on the hard thresholding, the soft fuzzy thresholding based on the histogram separation into segmentation classes via the fuzzy triangular functions represents a sensitive regional segmentation even in the non-contrast environment. We have proposed the soft segmentation where the fuzzy sets are driven by the ABC genetic algorithm to optimal fuzzy class&apos;s distribution regarding the knee tissues characteristics. Consequently, the spatial aggregation is employed to taking advantage the spatial dependences which allows for modification the original fuzzy membership function. This procedure ensures the correct pixel&apos;s classification especially when the noise pixels are present. Such multiregional segmentation makes a mathematical model well separating the physiological cartilage from the early osteoarthritic spots which are highlighted in the model.

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

<a href="/cs/project/GA17-03037S" target="_blank" >GA17-03037S: Hodnocení investic do vývoje zdravotních prostředků</a><br>

Návaznosti

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

Rok uplatnění

2018

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 11056

ISBN

978-3-319-98445-2

ISSN

0302-9743

e-ISSN

1611-3349

Počet stran výsledku

10

Strana od-do

479-488

Název nakladatele

Elsevier

Místo vydání

Oxford

Místo konání akce

Bristol

Datum konání akce

5. 9. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000458812900045