Comparative Study of Denoising and Segmentation Techniques for Accurate Brain Tumor Detection in MRI

Abstract

Background: Brain tumor incidence is on the rise each year, with more than 130 identified types. Precise segmentation models play a vital role in the diagnosis and treatment of brain tumors. This study specifically investigates the utilization of diffusion-based denoising techniques and thresholding methods for segmenting brain tumors from MRI images. Objective: The objective of this study is to examine and compare the efficacy of the Perona-malik and Weickert diffusion techniques in denoising brain MRI images. Additionally, the study aims to assess their performance in threshold-based segmentation of brain tumors. Moreover, it also aims to evaluate the compatibility, benefits, and limitations of the Perona-Malik and Weickert diffusion methods in the denoising of brain MRI images and the effect of denoising on segmentation. Methods: In this study, the Perona-Malik and Weickert diffusion methods are employed to denoise brain MRI images. The denoised images are then subjected to thresholding using both binary and fuzzy approaches, utilizing a triangular membership function. The performance of the diffusion techniques is evaluated using metrics, such as Mean Square Error and Peak Signal to Noise Ratio. Additionally, segmentation models are assessed using metrics such as Dice Similarity Coefficient, Jaccard Similarity Coefficient, and Structural Similarity Measurement Index. Results: The Perona-Malik and Weickert diffusion methods exhibit compatibility with various types of noise, each having its own set of advantages and limitations. The Weickert diffusion method excels in preserving image structure and texture during thresholding. Conclusion: The study provides evidence for the effectiveness of diffusion-based denoising techniques in segmenting brain tumors from MRI images. Specifically, the Weickert diffusion method outperforms in preserving essential image characteristics during thresholding. Additionally, fuzzy thresholding proves to be more successful in accurately segmenting brain tumors. These findings contribute to the advancement of precise models for brain tumor segmentation, ultimately enhancing the diagnosis and treatment of these tumors.