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# GI Bleeding Detection

![GI Bleeding Detection Banner](https://github.com/yourusername/gi-bleeding-detection/raw/main/docs/images/banner.png?raw=true)

A Python application that uses computer vision and machine learning techniques to detect gastrointestinal (GI) bleeding in endoscopic images. This tool provides healthcare professionals with a visual aid for identifying potential bleeding regions in the GI tract.

## Features

- **Automated Bleeding Detection**: Uses color segmentation, K-means clustering, and HSV analysis to identify bleeding regions
- **User-Friendly Interface**: Simple GUI for easy image loading, analysis, and result saving
- **Visual Results**: Highlights potential bleeding areas on the original image
- **Quantitative Analysis**: Calculates bleeding area percentage and provides risk assessment
- **Report Generation**: Creates saveable reports for medical records and further analysis

## Screenshots

### Main Application Interface

![Main Interface](https://github.com/yourusername/gi-bleeding-detection/raw/main/docs/images/main-interface.png?raw=true)

### Analysis Results Example

![Analysis Results](https://github.com/yourusername/gi-bleeding-detection/raw/main/docs/images/analysis-results.png?raw=true)

### Bleeding Detection Visualization

**Original Endoscopic Image**  
![Original Image](https://github.com/yourusername/gi-bleeding-detection/raw/main/docs/images/original-image.png)

**Bleeding Detection Result**  
![Bleeding Detection](https://github.com/yourusername/gi-bleeding-detection/raw/main/docs/images/bleeding-detection.png?raw=true)

## Installation

### Prerequisites

- Python 3.8 or higher
- pip package manager

### Dependencies

- NumPy
- OpenCV (cv2)
- TkInter
- PIL (Pillow)
- scikit-learn
- Matplotlib

### Setup

1. Clone this repository:
   ```bash
   git clone https://github.com/yourusername/gi-bleeding-detection.git
   cd gi-bleeding-detection
   ```

2. Create a virtual environment (recommended):
   ```bash
   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   ```

3. Install dependencies:
   ```bash
   pip install -r requirements.txt
   ```

## Usage

1. Run the application:
   ```bash
   python gi_bleeding_detector.py
   ```

2. Click "Load Image" to select an endoscopic image.

3. Click "Analyze Image" to process the image and detect possible bleeding regions.

4. View the results, which include:
   - Visual highlighting of potential bleeding areas
   - Percentage of the image showing bleeding
   - Risk assessment based on bleeding percentage
   - Graphical representation of results

5. Click "Save Results" to export the analysis to your computer.

## How It Works

### Image Processing Pipeline

1. **Image Loading**: Loads and prepares the endoscopic image
2. **Color Segmentation**: Uses K-means clustering to group similar colored pixels
3. **HSV Conversion**: Converts to HSV color space for better color analysis
4. **Red Detection**: Applies color thresholds to detect red regions (potential bleeding)
5. **Quantification**: Calculates the percentage of the image containing bleeding
6. **Visualization**: Overlays the results on the original image

### Example Code

```python
def process_image(self):
    """Process the image to detect GI bleeding"""
    if self.original_image is None:
        return False
    
    # Convert to RGB for better color analysis
    image_rgb = cv2.cvtColor(self.original_image, cv2.COLOR_BGR2RGB)
    
    # Resize image for faster processing if needed
    resized = cv2.resize(image_rgb, (0, 0), fx=0.5, fy=0.5) if image_rgb.shape[0] > 1000 else image_rgb
    
    # Reshape the image for K-means
    pixels = resized.reshape(-1, 3).astype(np.float32)
    
    # Define criteria and apply K-means
    criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)
    k = 5  # Number of clusters
    _, labels, centers = cv2.kmeans(pixels, k, None, criteria, 10, cv2.KMEANS_RANDOM_CENTERS)
    
    # Convert back to uint8
    centers = np.uint8(centers)
    segmented_image = centers[labels.flatten()]
    segmented_image = segmented_image.reshape(resized.shape)
    
    # Detect red regions (potential bleeding)
    # Convert to HSV for better color segmentation
    hsv_img = cv2.cvtColor(segmented_image, cv2.COLOR_RGB2HSV)
    
    # Define range for red color in HSV
    lower_red1 = np.array([0, 120, 70])
    upper_red1 = np.array([10, 255, 255])
    lower_red2 = np.array([170, 120, 70])
    upper_red2 = np.array([180, 255, 255])
    
    # Create masks for red regions
    mask1 = cv2.inRange(hsv_img, lower_red1, upper_red1)
    mask2 = cv2.inRange(hsv_img, lower_red2, upper_red2)
    
    # Combine masks
    self.mask = cv2.bitwise_or(mask1, mask2)
    
    # Calculate bleeding percentage
    total_pixels = self.mask.size
    bleeding_pixels = cv2.countNonZero(self.mask)
    self.bleeding_percentage = (bleeding_pixels / total_pixels) * 100
```

## Performance Evaluation

The application was tested on a dataset of 100 endoscopic images with the following results:

| Metric | Value |
|--------|-------|
| Sensitivity | 92.3% |
| Specificity | 88.7% |
| Accuracy | 90.5% |

*Note: These are example metrics. Actual performance may vary based on image quality and bleeding characteristics.*

## Limitations

- The detection accuracy depends on image quality and lighting conditions
- The algorithm may produce false positives in images with naturally red tissues
- Not intended to replace clinical judgment, but to serve as a supplementary tool
- Performance may vary across different endoscopic equipment

## Future Improvements

- [ ] Implement deep learning models for improved detection accuracy
- [ ] Add support for video analysis of endoscopic procedures
- [ ] Develop severity classification of bleeding regions
- [ ] Integrate with medical records systems
- [ ] Add automatic report generation with medical terminology

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (`git checkout -b feature/amazing-feature`)
3. Commit your changes (`git commit -m 'Add some amazing feature'`)
4. Push to the branch (`git push origin feature/amazing-feature`)
5. Open a Pull Request

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## Citation

If you use this software in your research, please cite:

```
@software{gi_bleeding_detection,
  author = {Your Name},
  title = {GI Bleeding Detection},
  year = {2025},
  url = {https://github.com/yourusername/gi-bleeding-detection}
}
```

## Acknowledgements

- Special thanks to medical professionals at [Hospital/Institution Name] for providing test images and validation
- [OpenCV](https://opencv.org/) library for computer vision algorithms
- [scikit-learn](https://scikit-learn.org/) for machine learning components

	a/README.md		b/README.md
1	# GI Bleeding Detection	1	# GI Bleeding Detection
2		2
3	![GI Bleeding Detection Banner](https://github.com/yourusername/gi-bleeding-detection/raw/main/docs/images/banner.png)	3	![GI Bleeding Detection Banner](https://github.com/yourusername/gi-bleeding-detection/raw/main/docs/images/banner.png?raw=true)
4		4
5	A Python application that uses computer vision and machine learning techniques to detect gastrointestinal (GI) bleeding in endoscopic images. This tool provides healthcare professionals with a visual aid for identifying potential bleeding regions in the GI tract.	5	A Python application that uses computer vision and machine learning techniques to detect gastrointestinal (GI) bleeding in endoscopic images. This tool provides healthcare professionals with a visual aid for identifying potential bleeding regions in the GI tract.
6		6
7	## Features	7	## Features
8		8
9	- Automated Bleeding Detection: Uses color segmentation, K-means clustering, and HSV analysis to identify bleeding regions	9	- Automated Bleeding Detection: Uses color segmentation, K-means clustering, and HSV analysis to identify bleeding regions
10	- User-Friendly Interface: Simple GUI for easy image loading, analysis, and result saving	10	- User-Friendly Interface: Simple GUI for easy image loading, analysis, and result saving
11	- Visual Results: Highlights potential bleeding areas on the original image	11	- Visual Results: Highlights potential bleeding areas on the original image
12	- Quantitative Analysis: Calculates bleeding area percentage and provides risk assessment	12	- Quantitative Analysis: Calculates bleeding area percentage and provides risk assessment
13	- Report Generation: Creates saveable reports for medical records and further analysis	13	- Report Generation: Creates saveable reports for medical records and further analysis
14		14
15	## Screenshots	15	## Screenshots
16		16
17	### Main Application Interface	17	### Main Application Interface
18		18
19	![Main Interface](https://github.com/yourusername/gi-bleeding-detection/raw/main/docs/images/main-interface.png)	19	![Main Interface](https://github.com/yourusername/gi-bleeding-detection/raw/main/docs/images/main-interface.png?raw=true)
20		20
21	### Analysis Results Example	21	### Analysis Results Example
22		22
23	![Analysis Results](https://github.com/yourusername/gi-bleeding-detection/raw/main/docs/images/analysis-results.png)	23	![Analysis Results](https://github.com/yourusername/gi-bleeding-detection/raw/main/docs/images/analysis-results.png?raw=true)
24		24
25	### Bleeding Detection Visualization	25	### Bleeding Detection Visualization
26		26
27	Original Endoscopic Image	27	Original Endoscopic Image
28	![Original Image](https://github.com/yourusername/gi-bleeding-detection/raw/main/docs/images/original-image.png)	28	![Original Image](https://github.com/yourusername/gi-bleeding-detection/raw/main/docs/images/original-image.png)
29		29
30	Bleeding Detection Result	30	Bleeding Detection Result
31	![Bleeding Detection](https://github.com/yourusername/gi-bleeding-detection/raw/main/docs/images/bleeding-detection.png)	31	![Bleeding Detection](https://github.com/yourusername/gi-bleeding-detection/raw/main/docs/images/bleeding-detection.png?raw=true)
32		32
33	## Installation	33	## Installation
34		34
35	### Prerequisites	35	### Prerequisites
36		36
37	- Python 3.8 or higher	37	- Python 3.8 or higher
38	- pip package manager	38	- pip package manager
39		39
40	### Dependencies	40	### Dependencies
41		41
42	- NumPy	42	- NumPy
43	- OpenCV (cv2)	43	- OpenCV (cv2)
44	- TkInter	44	- TkInter
45	- PIL (Pillow)	45	- PIL (Pillow)
46	- scikit-learn	46	- scikit-learn
47	- Matplotlib	47	- Matplotlib
48		48
49	### Setup	49	### Setup
50		50
51	1. Clone this repository:	51	1. Clone this repository:
52	```bash	52	```bash
53	git clone https://github.com/yourusername/gi-bleeding-detection.git	53	git clone https://github.com/yourusername/gi-bleeding-detection.git
54	cd gi-bleeding-detection	54	cd gi-bleeding-detection
55	```	55	```
56		56
57	2. Create a virtual environment (recommended):	57	2. Create a virtual environment (recommended):
58	```bash	58	```bash
59	python -m venv venv	59	python -m venv venv
60	source venv/bin/activate # On Windows: venv\Scripts\activate	60	source venv/bin/activate # On Windows: venv\Scripts\activate
61	```	61	```
62		62
63	3. Install dependencies:	63	3. Install dependencies:
64	```bash	64	```bash
65	pip install -r requirements.txt	65	pip install -r requirements.txt
66	```	66	```
67		67
68	## Usage	68	## Usage
69		69
70	1. Run the application:	70	1. Run the application:
71	```bash	71	```bash
72	python gi_bleeding_detector.py	72	python gi_bleeding_detector.py
73	```	73	```
74		74
75	2. Click "Load Image" to select an endoscopic image.	75	2. Click "Load Image" to select an endoscopic image.
76		76
77	3. Click "Analyze Image" to process the image and detect possible bleeding regions.	77	3. Click "Analyze Image" to process the image and detect possible bleeding regions.
78		78
79	4. View the results, which include:	79	4. View the results, which include:
80	- Visual highlighting of potential bleeding areas	80	- Visual highlighting of potential bleeding areas
81	- Percentage of the image showing bleeding	81	- Percentage of the image showing bleeding
82	- Risk assessment based on bleeding percentage	82	- Risk assessment based on bleeding percentage
83	- Graphical representation of results	83	- Graphical representation of results
84		84
85	5. Click "Save Results" to export the analysis to your computer.	85	5. Click "Save Results" to export the analysis to your computer.
86		86
87	## How It Works	87	## How It Works
88		88
89	### Image Processing Pipeline	89	### Image Processing Pipeline
90		90
91	1. Image Loading: Loads and prepares the endoscopic image	91	1. Image Loading: Loads and prepares the endoscopic image
92	2. Color Segmentation: Uses K-means clustering to group similar colored pixels	92	2. Color Segmentation: Uses K-means clustering to group similar colored pixels
93	3. HSV Conversion: Converts to HSV color space for better color analysis	93	3. HSV Conversion: Converts to HSV color space for better color analysis
94	4. Red Detection: Applies color thresholds to detect red regions (potential bleeding)	94	4. Red Detection: Applies color thresholds to detect red regions (potential bleeding)
95	5. Quantification: Calculates the percentage of the image containing bleeding	95	5. Quantification: Calculates the percentage of the image containing bleeding
96	6. Visualization: Overlays the results on the original image	96	6. Visualization: Overlays the results on the original image
97		97
98	### Example Code	98	### Example Code
99		99
100	```python	100	```python
101	def process_image(self):	101	def process_image(self):
102	"""Process the image to detect GI bleeding"""	102	"""Process the image to detect GI bleeding"""
103	if self.original_image is None:	103	if self.original_image is None:
104	return False	104	return False
105		105
106	# Convert to RGB for better color analysis	106	# Convert to RGB for better color analysis
107	image_rgb = cv2.cvtColor(self.original_image, cv2.COLOR_BGR2RGB)	107	image_rgb = cv2.cvtColor(self.original_image, cv2.COLOR_BGR2RGB)
108		108
109	# Resize image for faster processing if needed	109	# Resize image for faster processing if needed
110	resized = cv2.resize(image_rgb, (0, 0), fx=0.5, fy=0.5) if image_rgb.shape[0] > 1000 else image_rgb	110	resized = cv2.resize(image_rgb, (0, 0), fx=0.5, fy=0.5) if image_rgb.shape[0] > 1000 else image_rgb
111		111
112	# Reshape the image for K-means	112	# Reshape the image for K-means
113	pixels = resized.reshape(-1, 3).astype(np.float32)	113	pixels = resized.reshape(-1, 3).astype(np.float32)
114		114
115	# Define criteria and apply K-means	115	# Define criteria and apply K-means
116	criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)	116	criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)
117	k = 5 # Number of clusters	117	k = 5 # Number of clusters
118	_, labels, centers = cv2.kmeans(pixels, k, None, criteria, 10, cv2.KMEANS_RANDOM_CENTERS)	118	_, labels, centers = cv2.kmeans(pixels, k, None, criteria, 10, cv2.KMEANS_RANDOM_CENTERS)
119		119
120	# Convert back to uint8	120	# Convert back to uint8
121	centers = np.uint8(centers)	121	centers = np.uint8(centers)
122	segmented_image = centers[labels.flatten()]	122	segmented_image = centers[labels.flatten()]
123	segmented_image = segmented_image.reshape(resized.shape)	123	segmented_image = segmented_image.reshape(resized.shape)
124		124
125	# Detect red regions (potential bleeding)	125	# Detect red regions (potential bleeding)
126	# Convert to HSV for better color segmentation	126	# Convert to HSV for better color segmentation
127	hsv_img = cv2.cvtColor(segmented_image, cv2.COLOR_RGB2HSV)	127	hsv_img = cv2.cvtColor(segmented_image, cv2.COLOR_RGB2HSV)
128		128
129	# Define range for red color in HSV	129	# Define range for red color in HSV
130	lower_red1 = np.array([0, 120, 70])	130	lower_red1 = np.array([0, 120, 70])
131	upper_red1 = np.array([10, 255, 255])	131	upper_red1 = np.array([10, 255, 255])
132	lower_red2 = np.array([170, 120, 70])	132	lower_red2 = np.array([170, 120, 70])
133	upper_red2 = np.array([180, 255, 255])	133	upper_red2 = np.array([180, 255, 255])
134		134
135	# Create masks for red regions	135	# Create masks for red regions
136	mask1 = cv2.inRange(hsv_img, lower_red1, upper_red1)	136	mask1 = cv2.inRange(hsv_img, lower_red1, upper_red1)
137	mask2 = cv2.inRange(hsv_img, lower_red2, upper_red2)	137	mask2 = cv2.inRange(hsv_img, lower_red2, upper_red2)
138		138
139	# Combine masks	139	# Combine masks
140	self.mask = cv2.bitwise_or(mask1, mask2)	140	self.mask = cv2.bitwise_or(mask1, mask2)
141		141
142	# Calculate bleeding percentage	142	# Calculate bleeding percentage
143	total_pixels = self.mask.size	143	total_pixels = self.mask.size
144	bleeding_pixels = cv2.countNonZero(self.mask)	144	bleeding_pixels = cv2.countNonZero(self.mask)
145	self.bleeding_percentage = (bleeding_pixels / total_pixels) * 100	145	self.bleeding_percentage = (bleeding_pixels / total_pixels) * 100
146	```	146	```
147		147
148	## Performance Evaluation	148	## Performance Evaluation
149		149
150	The application was tested on a dataset of 100 endoscopic images with the following results:	150	The application was tested on a dataset of 100 endoscopic images with the following results:
151		151
152	\| Metric \| Value \|	152	\| Metric \| Value \|
153	\|--------\|-------\|	153	\|--------\|-------\|
154	\| Sensitivity \| 92.3% \|	154	\| Sensitivity \| 92.3% \|
155	\| Specificity \| 88.7% \|	155	\| Specificity \| 88.7% \|
156	\| Accuracy \| 90.5% \|	156	\| Accuracy \| 90.5% \|
157		157
158	Note: These are example metrics. Actual performance may vary based on image quality and bleeding characteristics.	158	Note: These are example metrics. Actual performance may vary based on image quality and bleeding characteristics.
159		159
160	## Limitations	160	## Limitations
161		161
162	- The detection accuracy depends on image quality and lighting conditions	162	- The detection accuracy depends on image quality and lighting conditions
163	- The algorithm may produce false positives in images with naturally red tissues	163	- The algorithm may produce false positives in images with naturally red tissues
164	- Not intended to replace clinical judgment, but to serve as a supplementary tool	164	- Not intended to replace clinical judgment, but to serve as a supplementary tool
165	- Performance may vary across different endoscopic equipment	165	- Performance may vary across different endoscopic equipment
166		166
167	## Future Improvements	167	## Future Improvements
168		168
169	- [ ] Implement deep learning models for improved detection accuracy	169	- [ ] Implement deep learning models for improved detection accuracy
170	- [ ] Add support for video analysis of endoscopic procedures	170	- [ ] Add support for video analysis of endoscopic procedures
171	- [ ] Develop severity classification of bleeding regions	171	- [ ] Develop severity classification of bleeding regions
172	- [ ] Integrate with medical records systems	172	- [ ] Integrate with medical records systems
173	- [ ] Add automatic report generation with medical terminology	173	- [ ] Add automatic report generation with medical terminology
174		174
175	## Contributing	175	## Contributing
176		176
177	Contributions are welcome! Please feel free to submit a Pull Request.	177	Contributions are welcome! Please feel free to submit a Pull Request.
178		178
179	1. Fork the repository	179	1. Fork the repository
180	2. Create your feature branch (`git checkout -b feature/amazing-feature`)	180	2. Create your feature branch (`git checkout -b feature/amazing-feature`)
181	3. Commit your changes (`git commit -m 'Add some amazing feature'`)	181	3. Commit your changes (`git commit -m 'Add some amazing feature'`)
182	4. Push to the branch (`git push origin feature/amazing-feature`)	182	4. Push to the branch (`git push origin feature/amazing-feature`)
183	5. Open a Pull Request	183	5. Open a Pull Request
184		184
185	## License	185	## License
186		186
187	This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.	187	This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
188		188
189	## Citation	189	## Citation
190		190
191	If you use this software in your research, please cite:	191	If you use this software in your research, please cite:
192		192
193	```	193	```
194	@software{gi_bleeding_detection,	194	@software{gi_bleeding_detection,
195	author = {Your Name},	195	author = {Your Name},
196	title = {GI Bleeding Detection},	196	title = {GI Bleeding Detection},
197	year = {2025},	197	year = {2025},
198	url = {https://github.com/yourusername/gi-bleeding-detection}	198	url = {https://github.com/yourusername/gi-bleeding-detection}
199	}	199	}
200	```	200	```
201		201
202	## Acknowledgements	202	## Acknowledgements
203		203
204	- Special thanks to medical professionals at [Hospital/Institution Name] for providing test images and validation	204	- Special thanks to medical professionals at [Hospital/Institution Name] for providing test images and validation
205	- [OpenCV](https://opencv.org/) library for computer vision algorithms	205	- [OpenCV](https://opencv.org/) library for computer vision algorithms
206	- [scikit-learn](https://scikit-learn.org/) for machine learning components	206	- [scikit-learn](https://scikit-learn.org/) for machine learning components