Background: In image processing, image segmentation is a more challenging task due to different shapes, locations, image intensities, etc. Brain tumors are one of the most common diseases in the world. So, the detection and segmentation of brain tumors are important in the medical field.
Objective: The primary goal of this work is to use the proposed methodology to segment brain MRI images into tumor and non-tumor segments or pixels.
Methods: In this work, we first selected the MRI medical images from the BraTS2020 database and transferred them to the contrast enhancement phase. Then, we applied thresholding for contrast enhancement to enhance the visibility of structures like blood arteries, tumors, or abnormalities. After the contrast enhancement process, the images were transformed into the image denoising phase. In this phase, a fourth-order partial differential equation was used for image denoising. After the image denoising process, these images were passed on to the segmentation phase. In this segmentation phase, we used an elephant herding algorithm for centroid optimization and then applied the multi-kernel fuzzy c-means clustering for image segmentation.
Results: Peak signal-to-noise ratio, mean square error, sensitivity, specificity, and accuracy were used to assess the performance of the proposed methods. According to the findings, the proposed strategy produced better outcomes than the conventional methods.
Conclusion: Our proposed methodology was reported to be a more effective technique than existing techniques.
Keywords: Contrast enhancement; Multi-kernel fuzzy c-means clustering.; Optimization; Partial differential equation; Segmentation; Thresholding.
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