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Z2-$\gamma$: an Application of Zienkiewicz-Zhu Error Estimator to Brain Tumor Detection in MR Images
Version 1
: Received: 1 September 2022 / Approved: 2 September 2022 / Online: 2 September 2022 (09:48:07 CEST)
A peer-reviewed article of this Preprint also exists.
Falini, A. Z2-γ: An Application of Zienkiewicz-Zhu Error Estimator to Brain Tumor Detection in MR Images. J. Imaging 2022, 8, 301. Falini, A. Z2-γ: An Application of Zienkiewicz-Zhu Error Estimator to Brain Tumor Detection in MR Images. J. Imaging 2022, 8, 301.
Abstract
Brain tumors are abnormal cells growth in the brain tissues that can be cancerous, or not. In any case, they could be a very aggressive disease that should be detected as early as possible. Usually, magnetic resonance imaging (MRI) is the main tool commonly adopted by neurologists and radiologists to identify and classify any possible anomalies present in the brain anatomy. In the present work, an automatic unsupervised method called Z2-$\gamma$, based on the use of adaptive finite-elements and suitable pre-processing and post-processing techniques, is introduced. In particular, the proposed methodology is able to automatically classify whether a given MR image represents a healthy or a diseased brain and in this latter case, is able to locate the tumor area.
Keywords
brain tumor detection; finite-elements; adaptivity; morphological transformation
Subject
Computer Science and Mathematics, Applied Mathematics
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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