Intensity inhomogeneity is a significant issue in magnetic resonance imaging (MRI), where the presence of bias field causes distortions in pixel values, resulting in inconsistent and erroneous intensities across the image. This artifact not only hampers accurate diagnosis by radiologists but also negatively impacts the performance of computer-aided diagnosis algorithms, particularly in tasks like segmentation. In our proposed approach, we use a hybrid technique called KIFCM, which integrates K-means and Fuzzy C-means to enhance brain tumor segmentation. K-means provides computational efficiency, while Fuzzy C-means improves accuracy by detecting missed tumor cells. We employ a bias correction method based on the level set framework, removing noise with a median filter and applying the hybrid KIFCM technique for optimal segmentation. Our method effectively addresses intensity variation challenges …