Detecting brain tumors at an early stage is a challenging task for doctors. MRI images are often prone to noise and environmental interference, making it difficult to identify tumors and their causes. To address this issue, we propose a system that detects brain tumors from MRI images with high accuracy.
System Overview:
The system works by converting MRI images into grayscale and then applying filters to remove noise and other environmental interferences. The user simply needs to upload the MRI image, and the system will process it through various image processing techniques. We apply a unique algorithm designed to detect brain tumors in the images.
One challenge in early-stage tumor detection is that the edges of the tumor are not clearly defined, which is why we employ image segmentation to enhance edge detection. This segmentation process aids in identifying the tumor by isolating the region of interest. We also implement multiple image filtering techniques to ensure the highest accuracy in tumor detection.
The system is built using MATLAB, ensuring reliability and efficiency in processing MRI images.
Features:
- User inputs MRI images for analysis.
- Conversion of the image into grayscale for further processing.
- Removal of noise and environmental interference through filtering.
- Application of advanced image processing techniques for tumor detection.
- Use of image segmentation to sharpen edges and detect tumors more effectively.
Software:
- MATLAB Version: R2013a
Hardware:
- Processor: Intel i3
- Operating System: Windows 7 or above
Advantages:
- The system leverages image segmentation and multiple filtering techniques for enhanced accuracy in tumor detection.
Disadvantages:
- The system works exclusively with MRI images.
Applications:
- Hospitals
- Medical research
- Diagnostic centers
