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Ιδρυματικό Αποθετήριο [SANDBOX]
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| URI | http://purl.tuc.gr/dl/dias/E14EAFBC-9659-4279-B0EC-B19B2DEF118A | - |
| Αναγνωριστικό | https://ieeexplore.ieee.org/document/7738197/ | - |
| Αναγνωριστικό | https://doi.org/10.1109/IST.2016.7738197 | - |
| Γλώσσα | en | - |
| Μέγεθος | 6 pages | en |
| Τίτλος | Recursive-mode K-means clustering for self-organization of dynamic imaging data | en |
| Δημιουργός | Vourlaki Ioanna-Theoni | en |
| Δημιουργός | Βουρλακη Ιωαννα-Θεωνη | el |
| Δημιουργός | Livanos Georgios | en |
| Δημιουργός | Λιβανος Γεωργιος | el |
| Δημιουργός | Giakoumakis Theodoros-Marios | en |
| Δημιουργός | Γιακουμακης Θεοδωρος-Μαριος | el |
| Δημιουργός | Zervakis Michail | en |
| Δημιουργός | Ζερβακης Μιχαηλ | el |
| Δημιουργός | Giakos George C. | en |
| Δημιουργός | Balas Costas | en |
| Δημιουργός | Μπαλας Κωστας | el |
| Εκδότης | Institute of Electrical and Electronics Engineers | en |
| Περίληψη | The aim of this study was to develop a novel algorithmic scheme for self-organizing data, adopting an recursive-mode k-means clustering approach. The proposed methodology attempts to refine and improve the clustering result by sequentially updating centers on the basis of their present and previous positions, exploiting both prior expert knowledge and posterior data information from the statistical distribution of the examined population. The performance of the implemented algorithm is evaluated on Dynamic Imaging data from cervical tissue, examining numerous sample curves representing the temporal response of tissue areas under the aceto-whitening effect. In comparison to the performance of classical k-means approach, the preliminary results of this study indicate the outperformance of the proposed iterative scheme. The primary benefit is attributed to the center improvement strategy against the center replacement methodology enforced in the classical approach. The performance and conceptual integration of knowledge justify the proposed update strategy as an efficient data grouping and classification tool, revealing a proposing potential to tissue evaluation and disease characterization applications. | en |
| Τύπος | Πλήρης Δημοσίευση σε Συνέδριο | el |
| Τύπος | Conference Full Paper | en |
| Άδεια Χρήσης | http://creativecommons.org/licenses/by/4.0/ | en |
| Ημερομηνία | 2018-06-28 | - |
| Ημερομηνία Δημοσίευσης | 2016 | - |
| Θεματική Κατηγορία | Cancer diagnosis | en |
| Θεματική Κατηγορία | Data self-organization | en |
| Θεματική Κατηγορία | Dynamic imaging | en |
| Θεματική Κατηγορία | Recursive-mode clustering | en |
| Βιβλιογραφική Αναφορά | I. Vourlaki, G. Livanos, T. Giakoumakis, M. Zervakis, G. Giakos and C. Balas, "Recursive-mode K-means clustering for self-organization of dynamic imaging data," in IEEE International Conference on Imaging Systems and Techniques, 2016, pp. 54-59. doi: 10.1109/IST.2016.7738197 | en |
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