Fuzzy Segmentation Driven by Modified ABC Algorithm Using Cartilage Features Completed by Spatial Aggregation: Modeling of Early Cartilage Loss
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Fuzzy Segmentation Driven by Modified ABC Algorithm Using Cartilage Features Completed by Spatial Aggregation: Modeling of Early Cartilage Loss
Original language description
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'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'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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GA17-03037S" target="_blank" >GA17-03037S: Investment evaluation of medical device development</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
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
Number of pages
10
Pages from-to
479-488
Publisher name
Elsevier
Place of publication
Oxford
Event location
Bristol
Event date
Sep 5, 2018
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000458812900045