Abstract
An important diagnostic technique for providing accurate information about the spatial distribution of brain soft tissues non-invasively is magnetic resonance (MR) imaging. In MR images, different imaging artifacts give rise to uncertainties in brain volume segmentation into major soft tissue classes; as well as in extracting brain tumor and evaluating its malignancy state. Among various soft computing techniques, rough sets provide a powerful tool to handle uncertainties and incompleteness associated with data, while fuzzy set serves as an analytical tool for dealing with uncertainty that arises due to the overlapping characteristics in the data. In this regard, the paper presents a brief review on the recent advances of rough-fuzzy hybridized approaches for brain MR volume segmentation, brain tumor detection and gradation.
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Acknowledgement
This work is an outcome of the R&D work undertaken in the project under the Visvesvaraya Ph.D. Scheme of Ministry of Electronics and Information Technology, Government of India, being implemented by Digital India Corporation.
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Maji, P., Roy, S. (2021). Rough-Fuzzy Segmentation of Brain MR Volumes: Applications in Tumor Detection and Malignancy Assessment. In: Ramanna, S., Cornelis, C., Ciucci, D. (eds) Rough Sets. IJCRS 2021. Lecture Notes in Computer Science(), vol 12872. Springer, Cham. https://doi.org/10.1007/978-3-030-87334-9_3
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DOI: https://doi.org/10.1007/978-3-030-87334-9_3
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