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--- |
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title: Veterinary DICOM MCP Server |
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emoji: 🐾 |
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colorFrom: blue |
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colorTo: green |
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sdk: gradio |
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sdk_version: 5.32.0 |
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app_file: app.py |
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pinned: false |
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license: apache-2.0 |
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tags: |
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- mcp-server-track |
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- veterinary |
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- medical-imaging |
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- hackathon-2025 |
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--- |
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# 🐾 Veterinary DICOM MCP Server |
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**First MCP server for veterinary medical imaging with species-specific DICOM enhancement using CLAHE, adaptive histogram equalization, and AI-powered quality assessment.** |
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## 🎥 Demo Video |
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**[🔗 Watch the Full Demo on Loom](https://www.loom.com/share/edc57face5614164ac822b110ba76c0e?sid=241e64ef-06fc-4f2e-9c3e-2bf32cb5b162)** |
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See the Veterinary DICOM MCP Server in action with real veterinary X-ray enhancement and analysis. |
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## 🎯 Hackathon Track 1: MCP Server Implementation |
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Transforms veterinary radiology with: |
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- **Species-specific algorithms** (canine, feline, equine, bovine) |
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- **Advanced image enhancement** (CLAHE, adaptive, contrast stretching) |
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- **AI quality metrics** (SSIM, PSNR, entropy analysis) |
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- **Agent-ready prompts** for diagnostic assessment |
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Built with Gradio + scikit-image + veterinary expertise from DIRU (Diagnostic Imaging Research Unit). |
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## 🔧 MCP Integration |
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Add this to your Claude Desktop or MCP client: |
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```json |
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{ |
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"mcpServers": { |
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"veterinary_dicom": { |
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"url": "https://huggingface.co/spaces/Agents-MCP-Hackathon/veterinary-dicom-mcp/gradio_api/mcp/sse" |
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} |
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} |
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} |
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``` |
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## 🚀 Features |
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### Species-Specific Enhancement |
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- **Canine**: Optimized parameters for dog radiology |
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- **Feline**: Cat-specific imaging adjustments |
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- **Equine**: Horse anatomy considerations |
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- **Bovine**: Cattle imaging optimization |
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### Advanced Algorithms |
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- **CLAHE**: Contrast Limited Adaptive Histogram Equalization |
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- **Adaptive**: Local contrast enhancement |
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- **Histogram**: Global equalization |
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- **Contrast Stretch**: Percentile-based improvement |
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- **Gamma Correction**: Brightness adjustment |
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### Quality Assessment |
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- **SSIM**: Structural Similarity Index |
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- **PSNR**: Peak Signal-to-Noise Ratio |
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- **Entropy**: Information content analysis |
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- **Edge Density**: Structure definition metrics |
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## 🏥 Clinical Applications |
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- **Diagnostic Imaging**: Enhanced visualization for veterinarians |
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- **Quality Control**: Automated image quality assessment |
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- **Research**: Standardized enhancement for studies |
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- **Education**: Teaching tool for veterinary radiology |
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## 🏆 Hackathon Innovation |
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This project represents the **first implementation** of: |
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- Veterinary-specific MCP server |
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- Species-aware medical image enhancement |
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- AI-powered diagnostic quality assessment |
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- Integration of veterinary domain expertise with modern AI tools |
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## 🔬 Technical Implementation |
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Built using: |
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- **Gradio**: Web interface and MCP server framework |
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- **scikit-image**: Advanced image processing algorithms |
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- **PyDICOM**: Medical imaging format support |
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- **NumPy/SciPy**: Scientific computing foundation |
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## 👥 Team |
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**DIRU - Diagnostic Imaging Research Unit** |
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Veterinary Medicine, University of Zurich |
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Combining veterinary medical expertise with cutting-edge AI technology to advance animal healthcare through improved diagnostic imaging. |
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## 📖 Usage |
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1. Upload a DICOM file or medical image |
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2. Select animal species (canine, feline, equine, bovine) |
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3. Choose enhancement method (CLAHE, adaptive, etc.) |
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4. Specify body region for targeted analysis |
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5. Receive enhanced image with quality metrics and AI assessment |
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## 🎯 MCP Tools Available |
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- `enhance_dicom_image`: Species-specific image enhancement |
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- `compare_enhancement_methods`: Multi-algorithm comparison |
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- Automated quality metrics and diagnostic prompts for AI agents |
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--- |
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**Developed for veterinary medicine with ❤️ and cutting-edge web technology** |
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**Gradio Agents & MCP Hackathon 2025 - Track 1 Submission** |
