Abstract
Precise segmentation of bone cancer is an important step for several applications. However, the achievement of this task has proven problematic due to lack of contrast and the non homogeneous intensities in many modalities such as MRI and CT-scans. In this paper we investigate this line of research by introducing a new method for segmenting bone cancer. Our segmentation process involves different steps: a registration step of different image modalities, a fuzzy-possibilistic classification (FPCM) step and a final segmentation step based on a variational model. The registration and the FPCM algorithms are used to locate and to initialize accurately the deformable model that will evolve smoothly to delineate the expected tumor boundaries. Preliminary results show accurate and promising detection of the cancer region.
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Bourouis, S., Chennoufi, I., Hamrouni, K. (2013). Multimodal Bone Cancer Detection Using Fuzzy Classification and Variational Model. In: Ruiz-Shulcloper, J., Sanniti di Baja, G. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2013. Lecture Notes in Computer Science, vol 8258. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41822-8_22
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DOI: https://doi.org/10.1007/978-3-642-41822-8_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41821-1
Online ISBN: 978-3-642-41822-8
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