Multiregional Soft Segmentation Driven by Modified ABC Algorithm and Completed by Spatial Aggregation: Volumetric, Spatial Modelling and Features Extraction of Articular Cartilage Early Loss
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F18%3A10241593" target="_blank" >RIV/61989100:27240/18:10241593 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-319-75420-8_37" target="_blank" >http://dx.doi.org/10.1007/978-3-319-75420-8_37</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-75420-8_37" target="_blank" >10.1007/978-3-319-75420-8_37</a>
Alternative languages
Result language
angličtina
Original language name
Multiregional Soft Segmentation Driven by Modified ABC Algorithm and Completed by Spatial Aggregation: Volumetric, Spatial Modelling and Features Extraction of Articular Cartilage Early Loss
Original language description
In a clinical practise of the orthopaedics and medical imaging systems, the early cartilage loss, and cartilage lesions are challenging tasks. Due to an insufficient contrast, such pathologies are badly observable by naked eyes. Furthermore, objectification and quantification of those pathological findings are usually only subjectively estimated without the SW support. We propose a multiregional segmentation model based on the histogram classification with using of a sequence of triangular fuzzy functions where each such function represents specific knee area. To ensure a robustness of the model, respective fuzzy class location is driven by the ABC (Artificial Bee Colony) genetic algorithm respecting statistical features of the physiological cartilage. In the second step of the algorithm, a spatial aggregation is applied in order to consider spatial relationships in every region to prevent the image noise deterioration. Such multiregional segmentation model allows for an extraction of significant features well corresponding with the early cartilage loss like is the cartilage volume.
Czech name
—
Czech description
—
Classification
Type
D - Article in proceedings
CEP classification
—
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 10752
ISBN
978-3-319-75419-2
ISSN
0302-9743
e-ISSN
1611-3349
Number of pages
10
Pages from-to
385-394
Publisher name
Springer
Place of publication
Cham
Event location
Dong Hoi
Event date
Mar 19, 2018
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000453510500037