Abstract
Human-assisted manual categorization of brain tumors is one of the most difficult tasks in medical image processing because incorrect prognosis and diagnosis might occur. Brain tumors vary in appearance, and there is a strong relationship between tumors and normal tissues. In this study, a thresholding approach was used to eliminate brain malignancies from 2D magnetic resonance brain images (MRI). We suggested an HOFilter for preprocessing MRI images that eliminates unnecessary noise and prepares the picture for tumor segmentation. We also compared our findings to those of other researchers on segmentation and detection and discovered that ours were better for a variety of methodologies. We got better results after using the highlighted object filter (HOFilter). To extract the appropriately segmented tumor from MRI images, edge detection, segmentation, and morphological techniques were applied. In our proposed model, we obtained an accuracy rate of 96.46% and a precision rate of 96.19%.
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Shemanto, T.H., Billah, L.B., Ibtesham, M.A. (2023). A Novel Method of Thresholding for Brain Tumor Segmentation and Detection. In: Ahmad, M., Uddin, M.S., Jang, Y.M. (eds) Proceedings of International Conference on Information and Communication Technology for Development. Studies in Autonomic, Data-driven and Industrial Computing. Springer, Singapore. https://doi.org/10.1007/978-981-19-7528-8_22
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DOI: https://doi.org/10.1007/978-981-19-7528-8_22
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