Abstract
One of the challenging tasks in the medical area is brain tumor segmentation which consists on the extraction process of tumor regions from images. Generally, this task is done manually by medical experts which is not always obvious due to the similarity between tumor and normal tissues and the high diversity in tumors appearance. Thus, automating medical image segmentation remains a real challenge which has attracted the attention of several researchers in last years. In this paper, we will focus on segmentation of Magnetic Resonance brain Images (MRI). Our idea is to consider this problem as a classification problem where the aim is to distinguish between normal and abnormal pixels on the basis of several features, namely intensities and texture. More precisely, we propose to use Support Vector Machine (SVM) which is within popular and well motivating classification methods. The experimental study will be carried on Gliomas dataset representing different tumor shapes, locations, sizes and image intensities.
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Ayachi, R., Ben Amor, N. (2009). Brain Tumor Segmentation Using Support Vector Machines. In: Sossai, C., Chemello, G. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2009. Lecture Notes in Computer Science(), vol 5590. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02906-6_63
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DOI: https://doi.org/10.1007/978-3-642-02906-6_63
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