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

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.

[1]  B. Stewart,et al.  World Cancer Report , 2003 .

[2]  H Greess,et al.  Oropharynx, oral cavity, floor of the mouth: CT and MRI. , 2000, European journal of radiology.

[3]  L. Schwartz,et al.  Lymph node segmentation from CT images using fast marching method. , 2004, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[4]  Colin Studholme,et al.  Automated 3-D registration of MR and CT images of the head , 1996, Medical Image Anal..

[5]  Jana Dornheim,et al.  Automatische Detektion von Lymphknoten in CT-Datensätzen des Halses , 2008, Bildverarbeitung für die Medizin.

[6]  Paul Suetens,et al.  Medical image registration using mutual information , 2003, Proc. IEEE.

[7]  Thomas Martin Deserno,et al.  Bildverarbeitung für die Medizin: Grundlagen, Modelle, Methoden, Anwendungen , 1997, Bildverarbeitung für die Medizin.

[8]  Bernhard Preim,et al.  Segmentation of neck lymph nodes in CT datasets with stable 3D mass-spring models segmentation of neck lymph nodes. , 2007, Academic radiology.

[9]  Wesley E. Snyder,et al.  Three-dimensional active surface approach to lymph node segmentation , 1999, Medical Imaging.

[10]  Paul A. Viola,et al.  Multi-modal volume registration by maximization of mutual information , 1996, Medical Image Anal..