Abstract
The segmentation of brain MRI is a critical step in many clinical applications. Artifacts inherent in this type of image, poor contrast, and substantial individual variances make it impossible to introduce a priori information. In this study, we offer a novel approach to brain MRI segmentation in which we integrate tissue and structural segmentation. We tested our proposed approach’s performance on both simulated and actual images, particularly its robustness to artifacts at low computation time. Statistical models appear to be a useful approach for medical image segmentation.
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Samir, B., Ahmed, H. (2023). Brain Tumor Segmentation Using Active Contour on Model Mumford-Shah Algorithm, Simple Standard Deviation, and Mathematical Morphology in Medical Images MRI. In: Motahhir, S., Bossoufi, B. (eds) Digital Technologies and Applications. ICDTA 2023. Lecture Notes in Networks and Systems, vol 669. Springer, Cham. https://doi.org/10.1007/978-3-031-29860-8_93
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DOI: https://doi.org/10.1007/978-3-031-29860-8_93
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