Year: 2025
Type: article
Abstract: Brain diseases are serious conditions that must not be ignored, as brain failure can pose a significant threat to overall health. Early detection and intervention are critical in managing various brain-related disorders. One of the primary diagnostic methods for detecting brain tumors and other neurological issues is MRI imaging. MRI is a preferred technique due to its efficiency, real-time imaging capabilities, and lack of radiation. However, challenges such as speckle noise, Gaussian noise, and other artifacts continue to compromise the quality of MRI images. As a result, enhancing image quality is crucial for accurate brain disorder diagnosis. To overcome these challenges, various imaging techniques are employed for preprocessing, noise reduction, and image enhancement. A key approach to obtaining high-quality images from noisy MRI data is image restoration and enhancement. Given the high-frequency characteristics of MRI, noise is often present in brain scans. Preprocessing plays an essential role in improving image quality by applying filters to eliminate noise. Techniques like mean, median, Wiener, and other filters are commonly used to address issues such as speckle, salt-and-pepper, and Gaussian noise. This research offers a comprehensive overview of various MRI image preprocessing and enhancement techniques, outlining their objectives and effectiveness.
Author: Mrs. Mandeep Kaur
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4407707673