A Combination of K-Means and Fuzzy C-Means for Brain Tumor Identification

Christy Atika Sari, Wellia Shinta Sari, Hidayah Rahmalan


Purpose: Magnetic Resonance Imaging is one of the health technologies used to scan the human body in order to get an image of an orgasm in the body. MRI imagery has a lot of noise that blends with the tumor object, so the tumor is quite difficult to detect automatically. In addition, it will be difficult to distinguish tumors from brain texture. Various methods have been carried out in previous studies. Methods: This study combines the K-Means method and Fuzzy C-Means (FCM) to detect tumors on MRI. The purpose of the combination is to get the advantages of each algorithm and minimize weaknesses. The method used is Contrast Adjustment using Fast Local Laplacian, K-Means FCM, Canny edge detection, Median Filter, and Morphological Area Selection. The dataset is taken from www.radiopedia.org. Data taken were 73 MRI of the brain, of which 57 MRIs with brain tumors and 16 MRIs of normal brain Evaluation of research results will be calculated using Confusion Matrix. Result: The accuracy obtained is 91.78%. Novelty: K-Means method and Fuzzy C-Means (FCM) to detect tumors on MRI.


MRI, Tumor Identification, Image Segmentation, K-Means, Fuzzy C-Means

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DOI: https://doi.org/10.15294/sji.v8i1.29357


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