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Feature Extraction from Medical Images for an Oral Cancer Reoccurrence Prediction Environment

Steger, Sebastian and Erdt, Marius and Chiari, Gianfranco and Sakas, Georgios (2009):
Feature Extraction from Medical Images for an Oral Cancer Reoccurrence Prediction Environment.
Springer, Berlin, Heidelberg, New York, In: World Congress on Medical Physics and Biomedical Engineering 2009. Proceedings DVD-ROM, In: IFMBE Proceedings; 25, [Conference or Workshop Item]

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.

Item Type: Conference or Workshop Item
Erschienen: 2009
Creators: Steger, Sebastian and Erdt, Marius and Chiari, Gianfranco and Sakas, Georgios
Title: Feature Extraction from Medical Images for an Oral Cancer Reoccurrence Prediction Environment
Language: English
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.

Series Name: IFMBE Proceedings; 25
Publisher: Springer, Berlin, Heidelberg, New York
Uncontrolled Keywords: Segmentation, Rigid registration, Feature extraction, Predictions, Oral cancer
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
Event Title: World Congress on Medical Physics and Biomedical Engineering 2009. Proceedings DVD-ROM
Date Deposited: 12 Nov 2018 11:16
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