Trainable Image Segmentation Using Deep Neural Networks
| but.event.date | 28.04.2016 | cs |
| but.event.title | Student EEICT 2016 | cs |
| dc.contributor.author | Majtán, Martin | |
| dc.date.accessioned | 2018-07-10T12:48:13Z | |
| dc.date.available | 2018-07-10T12:48:13Z | |
| dc.date.issued | 2016 | cs |
| dc.description.abstract | This paper is focused on trainable segmentation of image with use of deep neural networks. In this paper, the principle of creating images from magnetic resonance, generating data with algorithm of sliding window, creating a data set used for training neural network and principal segmentation of image with neural network is described. In practical part the algorithm of sliding window is created for generating data from magnetic resonance images and created model of artificial neural network used for image segmentation. In the practical part was achieved accuracy of segmentation 64 %. | en |
| dc.format | text | cs |
| dc.format.extent | 177-179 | cs |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | Proceedings of the 22nd Conference STUDENT EEICT 2016. s. 177-179. ISBN 978-80-214-5350-0 | cs |
| dc.identifier.isbn | 978-80-214-5350-0 | |
| dc.identifier.uri | http://hdl.handle.net/11012/83909 | |
| dc.language.iso | sk | cs |
| dc.publisher | Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií | cs |
| dc.relation.ispartof | Proceedings of the 22nd Conference STUDENT EEICT 2016 | en |
| dc.relation.uri | http://www.feec.vutbr.cz/EEICT/ | cs |
| dc.rights | © Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií | cs |
| dc.rights.access | openAccess | en |
| dc.subject | neural network | en |
| dc.subject | deep learning | en |
| dc.subject | segmentation | en |
| dc.subject | MRI | en |
| dc.subject | DL4J | en |
| dc.subject | multiple sclerosis | en |
| dc.title | Trainable Image Segmentation Using Deep Neural Networks | en |
| dc.type.driver | conferenceObject | en |
| dc.type.status | Peer-reviewed | en |
| dc.type.version | publishedVersion | en |
| eprints.affiliatedInstitution.department | Fakulta elektrotechniky a komunikačních technologií | cs |
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