Brain_Tumor_Detector_App_in_MATLAB

A simple MATLAB GUI application for detecting brain tumors from MRI images using basic image processing techniques such as median filtering, Sobel edge detection, and region-based segmentation.

🚀 Overview

This interactive app allows users to:

• Upload an MRI image.
• Apply a Median Filter to reduce noise.
• Perform Edge Detection using a custom Sobel operator.
• Detect tumor regions using morphological processing and region properties.

The tool is built using MATLAB App Designer components, providing an intuitive and visually interactive platform for beginners to explore medical image processing.

🔬 How It Works

🧠 1. What is MRI?

MRI (Magnetic Resonance Imaging) is a powerful imaging technique used in the medical field to generate detailed pictures of organs and soft tissues, especially the brain.

📌 Key Points:

🧲 Uses strong magnetic fields and radio waves.

🧬 Excellent for visualizing soft tissues, like brain matter.

🎯 Helps identify abnormalities such as tumors.

🕳️ Tumors often appear as brighter or darker regions compared to the surrounding tissue.

✅ This contrast allows both doctors and computer programs to detect tumors efficiently.

🧹 2. Preprocessing with Median Filtering

Before processing, MRI images may contain noise (unwanted random variations in intensity). This can affect the accuracy of further steps like edge detection and segmentation.

📌 Median Filtering to the Rescue:

📉 Replaces each pixel with the median of its surrounding pixels (usually in a 3x3 or 5x5 window).

❌ Removes salt-and-pepper noise without blurring the image.

✅ Preserves edges better than averaging filters.

🧼 Result: A cleaner, denoised image ready for further analysis.

✂️ 3. Edge Detection Using Sobel Operator

After denoising, the app detects edges—the boundaries where image intensity changes sharply. This is important to localize tumors and other structures.

📌 Why Edges?

📐 Edges represent boundaries of different regions (like a tumor vs normal tissue).

🔍 Helps in identifying the shape and location of the tumor.

📌 Sobel Operator:

➕ Applies two filters (horizontal & vertical gradients).

🧮 Computes the gradient magnitude to find where intensity changes the most.

🌟 Produces an image with highlighted boundaries (edges) in white or bright regions.

⚠️ At this stage, we don't yet know what the structure is—but we know where the boundaries are.

🎯 4. Tumor Segmentation and Detection

Now that the structure is visible, it's time to identify which part of the image actually contains the tumor.

📌 Segmentation Process:

⚪ Thresholding: Convert the grayscale image into a binary image (white = possible tumor, black = background).

🧩 Labeling Regions: Use bwlabel to assign labels to all connected white regions.

📊 Analyze Regions: Use regionprops to measure each region’s:

Solidity: How compact or filled the shape is.

Area: How big the region is.

🧠 Select Tumor Region:

The largest, solid region is most likely the tumor.

Helps avoid small, irrelevant bright spots.

📌 Post-processing:

🧱 Apply dilation to enlarge the region slightly for better visualization.

🟡 Overlay the tumor boundary in yellow on the filtered MRI for final display.

🎉 Result: The app clearly shows the tumor region on the MRI scan.

✨ Features

• 🖼️ Load grayscale or RGB MRI scans
• 🧹 Noise reduction using median filtering
• 🧾 Custom Sobel Edge Detection
• 🧠 Tumor region segmentation
• 🖱️ Interactive GUI with reset and step-by-step processing

🛠️ Requirements

• MATLAB R2021a or newer (App Designer supported)
• Image Processing Toolbox

📦 Installation

1. Clone the repository:

   git clone https://github.com/yourusername/Brain-Tumor-Detector-App.git
   cd Brain-Tumor-Detector-App

2. Open Brain_Tumor_Detector_App_2.m in MATLAB.

3. Click Run to launch the app GUI.

🎮 Usage Instructions

1. Click on "Select MRI" to load an image (.jpg, .png, .bmp).
2. Click "Median Filter" to denoise the image.
3. Click "Edge Detect" to extract boundaries using the Sobel operator.
4. Click "Detect Tumor" to highlight the largest solid region assumed to be a tumor.
5. Click "Reset" to clear all images and restart the process.

📷 Screenshots

🏠 Main Interface

🧠 Tumor Detection Example

📂 Project Structure

Brain-Tumor-Detector-App/
│
├── Brain_Tumor_Detector_App_2.m     # Main app code
├── README.md                        # Project documentation
├── examples/                        # Sample MRI images (optional)
└── screenshots/                     # GUI screenshots (optional)

🧠 How Tumor is Detected?

1. The image is thresholded to create a binary mask.
2. Connected components are labeled.
3. Each region’s solidity and area are analyzed.
4. The largest solid region is assumed to be the tumor.
5. The tumor region is highlighted with a yellow boundary.

📜 License

This project is licensed under the MIT License. See LICENSE for more details.

🧑‍💻 Author

K. Lohitha Reddy