Synergy of Convolutional Neural Networks and Geometric Active Contours
Ładowanie...
Data
2021
Tytuł czasopisma
ISSN czasopisma
Tytuł tomu
Wydawca
Wydawnictwo Politechniki Łódzkiej
Lodz University of Technology Press
Lodz University of Technology Press
Abstrakt
Hybrid approach to machine learning techniques could potentially
provide improvements in image segmentation results. In this paper, a
model of cooperation of convolutional neural networks and geometric active
contours is proposed and developed. The novelty of the approach lies in combining
deep neural networks and active contour model in order to improve
CNN output results. The method is examined on the image segmentation
task and applied to the detection and extraction of nuclei of HL60 cell line.
The model had been tested on both 2-D and 3-D images. Because of feature
learning characteristics of convolutional neural networks, the proposed
solution should perform well in multiple scenarios and can be considered
generic.
Opis
Słowa kluczowe
convolutional neural networks, CNN, geometric active contours, GAC, image segmentation, biomedical applications, machine learning, splotowe sieci neuronowe, geometryczne aktywne kontury, segmentacja obrazu, zastosowania biomedyczne, uczenie maszynowe
Cytowanie
Tomaczyk A., Pankiv O., Szczepaniak Piotr S., Synergy of Convolutional Neural Networks and Geometric Active Contours. W: TEWI 2021 (Technology, Education, Knowledge, Innovation),Wojciechowski A. (Ed.), Napieralski P. (Ed.), Lipiński P. (Ed.)., Seria: Monografie PŁ;Nr 2378, Wydawnictwo Politechniki Łódzkiej, Łódź 2021, s. 207-215, ISBN 978-83-66741-10-2, DOI 10.34658/9788366741102.14.