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3 March 2007 A new general tumor segmentation framework based on radial basis function energy minimization with a validation study on LIDC lung nodules
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Abstract
In this paper we describe a new general tumor segmentation approach, which combines energy minimization methods with radial basis function surface modelling techniques. A tumor is mathematically described by a superposition of radial basis functions. In order to find the optimal segmentation we minimize a certain energy functional. Similar to snake segmentation our energy functional is a weighted sum of an internal and an external energy. The internal energy is the bending energy of the surface and can be computed from the radial basis function coefficients directly. Unlike to snake segmentation we do not have to derive and solve Euler-Lagrange equations. We can solve the minimization problem by standard optimization techniques. Our approach is not restricted to one single imaging modality and it can be applied to 2D, 3D or even 4D data. In addition, our segmentation method makes several simple and intuitive user interactions possible. For instance, we can enforce interpolation of certain user defined points. We validate our new method with lung nodules on CT data. A validation on clinical data is carried out with the 91 publicly available CT lung images provided by the lung image database consortium (LIDC). The LIDC also provides ground truth lists by 4 different radiologists. We discuss the inter-observer variability of the 4 radiologists and compare their segmentations with the segmentation results of the presented algorithm.
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Roland Opfer and Rafael Wiemker "A new general tumor segmentation framework based on radial basis function energy minimization with a validation study on LIDC lung nodules", Proc. SPIE 6512, Medical Imaging 2007: Image Processing, 651217 (3 March 2007); https://doi.org/10.1117/12.708183
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