Szczegóły publikacji
Opis bibliograficzny
Tissue individual signatures through machine learning analysis of ultrasound images / Katarzyna HERYAN, Joanna SORYSZ, Michael FRIEBE // W: EMBC 2025 [Dokument elektroniczny] : 2025 47th annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC) : Copenhagen, Denmark, 14–18 July 2025 : proceedings. — Wersja do Windows. — Dane tekstowe. — Piscataway : Institute of Electrical and Electronics Engineers, cop. 2025. — ( Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society ; ISSN 1094-687X ). — e-ISBN: 979-8-3315-8618-8. — S. [1–5]. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. [4–5], Abstr. — Publikacja dostępna online od: 2025-12-03
Autorzy (3)
Dane bibliometryczne
| ID BaDAP | 165247 |
|---|---|
| Data dodania do BaDAP | 2026-01-12 |
| Tekst źródłowy | URL |
| DOI | 10.1109/EMBC58623.2025.11254734 |
| Rok publikacji | 2025 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Institute of Electrical and Electronics Engineers (IEEE) |
| Konferencja | The Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2025 |
| Czasopismo/seria | Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society |
Abstract
Advancements in sensors, computing, and surgical data science have significantly enhanced minimally invasive procedure, offering benefits such as reduced trauma, faster recovery, and lower infection risk. Despite these improvements, challenges persist regarding intraoperative accuracy, safety, and localization of surgical tools. Ultrasound imaging, while widely used for real-time visualization, faces limitations such as artifacts and poor alignment with instruments during procedures. This study presents a preliminary framework for ultrasound-based classification of materials with tissue-like properties using machine learning algorithms. Artificial phantoms constructed from accessible materials and biological tissues were imaged with ultrasound. Extracted image features were preprocessed using Gabor, Tamura, and Hessian filters, followed by statistical parameterization. Classification models including Random Forest, Support Vector Machines, and Discriminant Analysis were evaluated using the F1 score, achieving up to 0.8 in classification performance. The results indicate that ultrasound imaging, combined with statistical learning, has the potential to differentiate materials with tissue-relevant properties. This approach may contribute to enhanced intraoperative navigation and real-time tissue characterization. Future work will include dataset expansion, statistical validation, and integration with clinical systems.
