The concept of an artificial neural network for the classification of atheromous plaques from ...

@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 a Jakub SKÁCEL. The concept of an artificial neural network for the classification of atheromous plaques from digitized segmented histological images. In \textit{ICENCO 2018 - 14th International Computer Engineering Conference: Secure Smart Societies}. New York: IEEE. s.~22-25. ISBN~978-1-5386-5117-9. doi:10.1109/ICENCO.2018.8636116. 2018.