Abstract
Abnormal tissue analysis in brain volumes is a difficult task, due to the shape variability that the brain tumors exhibit between patients. The main problem in these processes is that the common techniques use linear representations of the input data which makes unsuitable to model complex shapes as brain tumors. In this paper, we present a kernelized morphable model (3D-KMM) for brain tumor analysis in which the model variations are captured through nonlinear mappings by using kernel principal component analysis. We learn complex shape variations through a high-dimensional representation of the input data. Then from the trained model, we recover the pre-images from the features vectors and perform a non-rigid matching procedure to fit the modeled tumor to a given brain volume. The results show that by using a kernelized morphable model, the non-rigid properties (i.e., nonlinearities and shape variations) of the abnormal tissues can be learned. Finally, our approach proves to be more accurate than the classic morphable model for shape analysis.
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Notes
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- 2.
We use the implementation available at http://iso2mesh.sourceforge.net/cgi-bin/index.cgi.
- 3.
We set the shape of the training tumors \(\varvec{\varGamma }_{i}\) to have the same size, that means all vertices were adjusted to the smallest one.
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Acknowledgments
This research is developed under the project “Desarrollo de un sistema de soporte clínico basado en el procesamiento estócasitco para mejorar la resolución espacial de la resonancia magnética estructural y de difusión con aplicación al procedimiento de la ablación de tumores” financed by COLCIENCIAS with code 111074455860. H.F. García is funded by Colciencias under the program: Formación de alto nivel para la ciencia, la tecnología y la innovación - Convocatoria 617 de 2013 with code 111065740687. D. A. Jimenez Sierra is partially funded by the Vicerrectoria de Investigaciones, Innovación y Extensión with project code E6-18-7 and by Maestría en Ingeniería Eléctrica both from the Universidad Tecnológica de Pereira.
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Jimenez, D.A., García, H.F., Álvarez, A.M., Orozco, Á.A., Holguín, G. (2018). A Kernelized Morphable Model for 3D Brain Tumor Analysis. In: Campilho, A., Karray, F., ter Haar Romeny, B. (eds) Image Analysis and Recognition. ICIAR 2018. Lecture Notes in Computer Science(), vol 10882. Springer, Cham. https://doi.org/10.1007/978-3-319-93000-8_60
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