The concept of an artificial neural network for the
classification of atheromous plaques from digitized segmented
histological images

D 2018

The concept of an artificial neural network for the classification of atheromous plaques from digitized segmented histological images

BLAHUTA, Jiří, Petr ČERMÁK and Jakub SKÁCEL

Basic information

Original name

The concept of an artificial neural network for the classification of atheromous plaques from digitized segmented histological images

Authors

BLAHUTA, Jiří (203 Czech Republic, guarantor), Petr ČERMÁK (203 Czech Republic) and Jakub SKÁCEL (203 Czech Republic, belonging to the institution)

Edition

New York, ICENCO 2018 - 14th International Computer Engineering Conference: Secure Smart Societies, p. 22-25, 4 pp. 2018

Publisher

IEEE

Other information

Language

English

Type of outcome

Stať ve sborníku

Field of Study

20200 2.2 Electrical engineering, Electronic engineering, Information engineering

Country of publisher

United States of America

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

electronic version available online

References:

URL

RIV identification code

RIV/47813059:19240/18:A0000316

Organization unit

Faculty of Philosophy and Science in Opava

ISBN

978-1-5386-5117-9

ISSN

DOI

http://dx.doi.org/10.1109/ICENCO.2018.8636116

Keywords in English

ANN; Atheromatous plaque; Histological patterns; Image segmentation; Neural Networks

Abstract

V originále

This paper is dedicted to the concept of an artificial neural network (ANN) for the classification of atheromatous plaque based on digitized histological patterns of areas segmented by means of the Region Growing algorithm. For this purpose, a multi-layered feedforward ANN with supervised learning has been used to successfully classify the segmented areas accordingly. The first phase is focused to find an optimal method for image segmentation. The Region Growing algorithm allows us to separate continuously segmented regions. For each region, appropriate features are selected, which are put into the neural network. The goal of the ANN is to classify plaque patterns into four classes according to their features. The classes represent the following types of plaque: homogeneous, heterogeneous, calcified and that with a high ratio of fat. Successful plaque classification will be a helpful tool for long-term clinical projects, e.g. investigation of plaque composition.

Citovat

BLAHUTA, Jiří, Petr ČERMÁK and Jakub SKÁCEL. The concept of an artificial neural network for the classification of atheromous plaques from digitized segmented histological images. Online. In ICENCO 2018 - 14th International Computer Engineering Conference: Secure Smart Societies. New York: IEEE, 2018, p. 22-25. ISBN 978-1-5386-5117-9. Available from: https://dx.doi.org/10.1109/ICENCO.2018.8636116.

@inproceedings{30171,
   author = {Blahuta, Jiří and Čermák, Petr and Skácel, Jakub},
   address = {New York},
   booktitle = {ICENCO 2018 - 14th International Computer Engineering Conference: Secure Smart Societies},
   doi = {http://dx.doi.org/10.1109/ICENCO.2018.8636116},
   keywords = {ANN; Atheromatous plaque; Histological patterns; Image segmentation; Neural Networks},
   howpublished = {elektronická verze "online"},
   language = {eng},
   location = {New York},
   isbn = {978-1-5386-5117-9},
   pages = {22-25},
   publisher = {IEEE},
   title = {The concept of an artificial neural network for the classification of atheromous plaques from digitized segmented histological images},
   url = {https://ieeexplore.ieee.org/document/8636116},
   year = {2018}
}

TY  - JOUR
ID  - 30171
AU  - Blahuta, Jiří - Čermák, Petr - Skácel, Jakub
PY  - 2018
TI  - The concept of an artificial neural network for the classification of atheromous plaques from digitized segmented histological images
PB  - IEEE
CY  - New York
SN  - 9781538651179
KW  - ANN
KW  - Atheromatous plaque
KW  - Histological patterns
KW  - Image segmentation
KW  - Neural Networks
UR  - https://ieeexplore.ieee.org/document/8636116
L2  - https://ieeexplore.ieee.org/document/8636116
N2  - This paper is dedicted to the concept of an artificial neural network (ANN) for the classification of atheromatous plaque based on digitized histological patterns of areas segmented by means of the Region Growing algorithm. For this purpose, a multi-layered feedforward ANN with supervised learning has been used to successfully classify the segmented areas accordingly. The first phase is focused to find an optimal method for image segmentation. The Region Growing algorithm allows us to separate continuously segmented regions. For each region, appropriate features are selected, which are put into the neural network. The goal of the ANN is to classify plaque patterns into four classes according to their features. The classes represent the following types of plaque: homogeneous, heterogeneous, calcified and that with a high ratio of fat. Successful plaque classification will be a helpful tool for long-term clinical projects, e.g. investigation of plaque composition.
ER  -

BLAHUTA, Jiří, Petr ČERMÁK and Jakub SKÁCEL. The concept of an artificial neural network for the classification of atheromous plaques from digitized segmented histological images. Online. In \textit{ICENCO 2018 - 14th International Computer Engineering Conference: Secure Smart Societies}. New York: IEEE, 2018, p.~22-25. ISBN~978-1-5386-5117-9. Available from: https://dx.doi.org/10.1109/ICENCO.2018.8636116.

Detailed Information on Publication Record