What is it about?
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.
Featured Image
Photo by National Cancer Institute on Unsplash
Why is it important?
Here's a concise summary of the importance of each step: Selection from BraTS2020 Database: Ensures standardized and high-quality MRI images for reliable analysis. Contrast Enhancement: Improves visibility of structures like tumors and blood arteries by enhancing image contrast. Thresholding: Further refines visibility of specific structures, aiding in clearer analysis. Image Denoising with Fourth-order Partial Differential Equation: Removes image noise while preserving important features, ensuring cleaner data for analysis. Segmentation with Elephant Herding Algorithm and Multi-kernel Fuzzy C-means Clustering: Accurately divides the image into meaningful regions, essential for tasks like tumor detection and treatment planning. Each step is crucial for enhancing image quality, improving visibility of structures, and ensuring accurate analysis for better clinical outcomes.
Perspectives
The method showcases a thoughtful progression from image enhancement to noise reduction and finally to segmentation. It leverages both traditional and advanced techniques, suggesting a balance between tried-and-true methods and innovative approaches. The emphasis on accuracy and detail preservation throughout each step reflects a commitment to producing reliable and clinically relevant results. This method appears well-suited for tasks requiring precise image analysis, such as tumor detection and treatment planning, potentially leading to improved diagnostic capabilities and patient care.
Sreedhar Kollem
SR University
Read the Original
This page is a summary of: Segmentation of Brain MRI Images Using Multi-Kernel FCM EHO Method, Current Medical Imaging Formerly Current Medical Imaging Reviews, August 2023, Bentham Science Publishers,
DOI: 10.2174/1573405620666230822114029.
You can read the full text:
Contributors
The following have contributed to this page
