In medical image segmentation, the identification, characterization, and visualization of a tumor’s dimension and region are considered to be very crucial, tedious, and time-consuming tasks. In spite of intensive research, segmentation is still one of the most challenging problems in the medical field due to the variety of image content. In this paper, we propose a new hybrid method for detecting and segmenting tumors in T2-weighted magnetic resonance imaging (MRI) brain scans. The approach begins with an efficient thresholding technique, followed by conventional morphological filtering, and then applies the binary K-means clustering algorithm. Experimental results and performance metrics demonstrate that the proposed method effectively identifies and segments tumors in MRI brain scans with significant accuracy.
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Muhammad Saad,
saadawan2278@gmail.com,
Department of Basic Sciences and Islamiat, University of Engineering and Technology, Peshawar,
Pakistan.
Muhammad Saad “Brain Tumor Segmentation via Efficient Thresholding and Binary K-Means Integrationâ Vol. 12 Issue 09 PP. 175-182 September 2025. https:// doi.org/10.5281/zenodo.17054880
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