Detection of orbital floor fractures by principal component analysis
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27230%2F16%3A86098947" target="_blank" >RIV/61989100:27230/16:86098947 - isvavai.cz</a>
Alternative codes found
RIV/61989100:27240/16:86098947 RIV/61989100:27740/16:86098947 RIV/00843989:_____/16:E0106616
Result on the web
<a href="http://link.springer.com/chapter/10.1007/978-3-319-45378-1_12" target="_blank" >http://link.springer.com/chapter/10.1007/978-3-319-45378-1_12</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-45378-1_12" target="_blank" >10.1007/978-3-319-45378-1_12</a>
Alternative languages
Result language
angličtina
Original language name
Detection of orbital floor fractures by principal component analysis
Original language description
Principal component analysis (PCA) is a statistical method based on orthogonal transformation, which is used to convert possibly correlated datasets into linearly uncorrelated variables called principal components. PCA is one of the simplest methods based on the eigenvector analysis. This method is widely used in many fields, such as signal processing, quality control or mechanical engineering. In this paper, we present the use of PCA in area of medical image processing. In the medical image processing with subsequent reconstruction of 3D models, data from sources such as Computed Tomography (CT) or Magnetic Resonance Imagining (MRI) are used. Series of images representing axial slices of human body are stored in Digital Imaging and Communications in Medicine (DICOM) format. Physical properties of different body tissues are characterized by different shades of grey of each pixel correlated to the tissue density. Properties of each pixel are then used in image segmentation and subsequent creation of 3D model of human organs. Image segmentation splits digital image into regions with similar properties which are later used to create 3D model. In many cases accurate detections of edges of such objects are necessary. This could be for example the case of a tumour or orbital fracture identification. In this paper, identification of the orbital fracture using PCA method is presented as an example of application of the method in the area of medical image processing. (C) IFIP International Federation for Information Processing 2016.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Computer information systems and industrial management : 15th IFIP TC8 International Conference, CISIM 2016 : Vilnius, Lithuania, September 14-16, 2016 : proceedings
ISBN
978-3-319-45377-4
ISSN
0302-9743
e-ISSN
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Number of pages
10
Pages from-to
129-138
Publisher name
Springer
Place of publication
Cham
Event location
Vilnius
Event date
Sep 14, 2016
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
000388720000012