Detection of orbital floor fractures by principal component analysis

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/61989100:27240/16:86098947 RIV/61989100:27740/16:86098947 RIV/00843989:_____/16:E0106616

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Detection of orbital floor fractures by principal component analysis

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Detection of orbital floor fractures by principal component analysis

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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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

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

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

—

Počet stran výsledku

10

Strana od-do

129-138

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Vilnius

Datum konání akce

14. 9. 2016

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

000388720000012