Steger, Sebastian ; Erdt, Marius ; Chiari, Gianfranco ; Sakas, Georgios (2009)
Feature Extraction from Medical Images for an Oral Cancer Reoccurrence Prediction Environment.
World Congress on Medical Physics and Biomedical Engineering 2009. Proceedings DVD-ROM.
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
We present the concept of a novel image feature extraction approach that will be used to predict oral cancer reoccurrence in the scope of the NeoMark project. Based on current clinical practice, we propose several numeric image features that characterize tumors and lymph nodes. In order to (semi) automatically extract those features we introduce the following approach which is independent from human subjectivity: Registration and supervised segmentation of CT/MR images forms the base of the automated extraction of geometric and texture features of tumors and lymph nodes. In order to reduce the amount of user interaction during follow ups we incorporate the segmentation results of the previous examinations. The robustness and the numeric manner of the extracted features make them ideally suited as input for a sophisticated adaptive prediction environment that estimates the likelihood of oral cancer reoccurrence and assists the clinician to develop a treatment plan.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2009 |
| Autor(en): | Steger, Sebastian ; Erdt, Marius ; Chiari, Gianfranco ; Sakas, Georgios |
| Art des Eintrags: | Bibliographie |
| Titel: | Feature Extraction from Medical Images for an Oral Cancer Reoccurrence Prediction Environment |
| Sprache: | Englisch |
| Publikationsjahr: | 2009 |
| Verlag: | Springer, Berlin, Heidelberg, New York |
| Reihe: | IFMBE Proceedings; 25 |
| Veranstaltungstitel: | World Congress on Medical Physics and Biomedical Engineering 2009. Proceedings DVD-ROM |
| Kurzbeschreibung (Abstract): | We present the concept of a novel image feature extraction approach that will be used to predict oral cancer reoccurrence in the scope of the NeoMark project. Based on current clinical practice, we propose several numeric image features that characterize tumors and lymph nodes. In order to (semi) automatically extract those features we introduce the following approach which is independent from human subjectivity: Registration and supervised segmentation of CT/MR images forms the base of the automated extraction of geometric and texture features of tumors and lymph nodes. In order to reduce the amount of user interaction during follow ups we incorporate the segmentation results of the previous examinations. The robustness and the numeric manner of the extracted features make them ideally suited as input for a sophisticated adaptive prediction environment that estimates the likelihood of oral cancer reoccurrence and assists the clinician to develop a treatment plan. |
| Freie Schlagworte: | Segmentation, Rigid registration, Feature extraction, Predictions, Oral cancer |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme |
| Hinterlegungsdatum: | 12 Nov 2018 11:16 |
| Letzte Änderung: | 12 Nov 2018 11:16 |
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