Zobrazeno 1 - 10
of 16
pro vyhledávání: '"Pantelis Asvestas"'
Publikováno v:
Eng, Vol 5, Iss 3, Pp 1708-1736 (2024)
This study harnesses EEG signals to enable the real-time control of servo motors, utilizing the OpenBCI Community Dataset to identify and assess brainwave patterns related to motor imagery tasks. Specifically, the dataset includes EEG data from 52 su
Externí odkaz:
https://doaj.org/article/4ced32df34a94d4bbb36e0d2db2fcd79
Autor:
Spiros Kostopoulos, Nada Boci, Dionisis Cavouras, Antonios Tsagkalis, Maria Papaioannou, Alexandra Tsikrika, Dimitris Glotsos, Pantelis Asvestas, Eleftherios Lavdas
Publikováno v:
Journal of Imaging, Vol 9, Iss 11, p 252 (2023)
Accurate diagnosis and timely intervention are key to addressing common knee conditions effectively. In this work, we aim to identify textural changes in knee lesions based on bone marrow edema (BME), injury (INJ), and osteoarthritis (OST). One hundr
Externí odkaz:
https://doaj.org/article/8bc0db75868e4486ad50ceabccbf00db
Publikováno v:
Engineering Proceedings, Vol 50, Iss 1, p 7 (2023)
This research presents a machine learning model that predicts left, right, or no action using electroencephalography (EEG) signals extracted from two different wearable EEG headsets. The research aims to develop an accurate and efficient model by fol
Externí odkaz:
https://doaj.org/article/b116adc4dc554f95b56ad37a73c60219
Autor:
Spiros Kostopoulos, Dimitris Glotsos, Pantelis Asvestas, Christos Konstandinou, George Xenogiannopoulos, Konstantinos Sidiropoulos, Eirini-Konstantina Nikolatou, Konstantinos Perakis, Spyros Mantzouratos, Theophilos Sakkis, George Sakellaropoulos, George Nikiforidis, Dionisis Cavouras
Publikováno v:
Image Analysis and Stereology, Vol 35, Iss 3, Pp 137-148 (2016)
Malignant melanoma represents the most dangerous type of skin cancer. In this study we present an ensemble classification scheme, employing the mutual information, the cross-correlation and the clustering based on proximity of image features methods,
Externí odkaz:
https://doaj.org/article/58625809a2bd40eebb0236f8eac23d3b
Autor:
Dimitris Glotsos, Spiros Kostopoulos, Panagiotis Liaparinos, Pantelis Asvestas, Ioannis Kalatzis
Publikováno v:
Biomedical Engineering Education. 3:51-60
In this study, we have evaluated the real-world conditions, the job outlook and the job satisfaction in the Biomedical Engineering (BME) sector in Greece on the basis of the experience of about 12% of the graduates of the BME Department of the Univer
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b8e58283c86ee34b07bbaa9f142ca5ba
https://doi.org/10.1007/s43683-022-00088-x
https://doi.org/10.1007/s43683-022-00088-x
Vertebrae, IVD and spinal canal boundary extraction on MRI, utilizing CT-trained active shape models
Autor:
Michalis A. Savelonas, Pantelis Asvestas, Dimitrios Papageorgiou, Meletios Liaskos, George K. Matsopoulos
Publikováno v:
International Journal of Computer Assisted Radiology and Surgery. 16:2201-2214
PURPOSE Vertebrae, intervertebral disc (IVD) and spinal canal (SC) displacements are in the root of several spinal cord pathologies. The localization and boundary extraction of these structures, along with the quantification of their displacements, p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::641ee516b8b0c039f3ca65633bd30092
https://doi.org/10.1007/s11548-021-02502-1
https://doi.org/10.1007/s11548-021-02502-1
Autor:
Irene S. Karanasiou, Pantelis Asvestas, Errikos M. Ventouras, Ioannis Kakkos, George K. Matsopoulos
Publikováno v:
Medical & Biological Engineering & Computing. 58:573-587
The cognitive processing and detection of errors is important in the adaptation of the behavioral and learning processes. This brain activity is often reflected as distinct patterns of event-related potentials (ERPs) that can be employed in the detec
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fc74ad95dfc9b9da2b152eca5d0f4eb7
https://doi.org/10.1007/s11517-019-02116-5
https://doi.org/10.1007/s11517-019-02116-5
Autor:
Spiros Kostopoulos, Panagiota Ravazoula, Dionisis Cavouras, Pantelis Asvestas, Vassiliki Tzelepi, Dimitris Glotsos, George Sakellaropoulos, Ioannis Kalatzis, Sotiris Ouzounis, Angeliki Theodosi
Publikováno v:
Machine Vision and Applications. 32
To design a hybrid deep learning system (hDL-system) for discriminating low-grade from high-grade colorectal cancer (CRC) lesions, using immunohistochemically stained biopsy specimens for AIB1 expression. AIB1 has oncogenic function in tumour genesis
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1efe7f262912514cb1d217239d68b872
https://doi.org/10.1007/s00138-021-01184-8
https://doi.org/10.1007/s00138-021-01184-8
Autor:
Dionisis Cavouras, Angeliki Theodosi, Sotiris Ouzounis, Ioannis Kalatzis, George Sakellaropoulos, Spiros Kostopoulos, Panagiota Ravazoula, Dimitris Glotsos, Vassiliki Tzelepi, Pantelis Asvestas
Publikováno v:
Microscopy research and techniqueREFERENCES. 84(10)
Our purpose was to employ microscopy images of amplified in breast cancer 1 (AIB1)-stained biopsy material of patients with colorectal cancer (CRC) to: (a) find statistically significant differences (SSDs) in the texture and color of the epithelial g
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::97c7266173923b111b1e94aba48ce43f
https://pubmed.ncbi.nlm.nih.gov/33929071
https://pubmed.ncbi.nlm.nih.gov/33929071
Autor:
Spiros Kostopoulos, Pantelis Asvestas, Dimitris Glotsos, Ioannis Kalatzis, George Michail, Dionisis Cavouras, Christos Konstantinou, George Sakellaropoulos, Panagiota Ravazoula
Publikováno v:
Applied immunohistochemistrymolecular morphology : AIMM. 28(9)
Objectives The objective of this study was (a) to identify, by computer processing of digitized images of hematoxylin and eosin (H&E)-stained biopsy material of the cervix, differences in the structure of nuclei between high-risk (HR) and low-risk (L
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::53818981b45bb19d3ee4f33513a9b523
https://pubmed.ncbi.nlm.nih.gov/31876603
https://pubmed.ncbi.nlm.nih.gov/31876603