K-Means Segmentation and Normalized Histogram: An Effective Method for Detecting Brain Tumor from MRIs
Keywords:
Segmentation, Histogram, MRIs, ClusteringAbstract
In the medical field, brain tumors are evaluated using a technique known as magnetic resonance imaging or MRI. To eliminate the additive noise which is found in MRI images, including Gaussian, Salt & Pepper, and Speckle noise, this study employs a technique to examine and classifies image d-noising filters including Mean, Adaptive, Minimizing, UN-sharp masking filter and the Gaussian filters. The DE-noising efficiency of each method is investigated using PSNR and MSE. The effective brain tumor segmentation utilizing the normalized histogram and the K-means clustering algorithm is shown as a novel method. Support Vector Machine (SVM) is to provide accurate forecasting and classification.
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