{"id":48596011,"url":"https://github.com/leosolar8/dicom-viewer-image-analysis","last_synced_at":"2026-04-08T21:01:42.390Z","repository":{"id":316464973,"uuid":"1063499796","full_name":"LEOSOLAR8/dicom-viewer-image-analysis","owner":"LEOSOLAR8","description":"Comprehensive medical imaging management system with a DICOM viewer, HU conversion, 28 image filters, and dual database architecture (Qdrant + SQLite). Supports radiology report generation, patient data management, and vector similarity search.","archived":false,"fork":false,"pushed_at":"2025-09-24T18:00:12.000Z","size":1500,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2026-01-07T04:15:15.888Z","etag":null,"topics":["dicom-viewer","healthcare","image-analysis","medical-imaging","opencv-python","pydicom","pyqt5-gui","qdrant","radiology","sqlite3","vector-databases"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/LEOSOLAR8.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2025-09-24T17:57:30.000Z","updated_at":"2025-09-24T22:26:51.000Z","dependencies_parsed_at":"2025-09-26T00:20:00.524Z","dependency_job_id":null,"html_url":"https://github.com/LEOSOLAR8/dicom-viewer-image-analysis","commit_stats":null,"previous_names":["lssandes/dicom-viewer-image-analysis","leosolar8/dicom-viewer-image-analysis"],"tags_count":null,"template":false,"template_full_name":null,"purl":"pkg:github/LEOSOLAR8/dicom-viewer-image-analysis","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LEOSOLAR8%2Fdicom-viewer-image-analysis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LEOSOLAR8%2Fdicom-viewer-image-analysis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LEOSOLAR8%2Fdicom-viewer-image-analysis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LEOSOLAR8%2Fdicom-viewer-image-analysis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/LEOSOLAR8","download_url":"https://codeload.github.com/LEOSOLAR8/dicom-viewer-image-analysis/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LEOSOLAR8%2Fdicom-viewer-image-analysis/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31573788,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-08T14:31:17.711Z","status":"ssl_error","status_checked_at":"2026-04-08T14:31:17.202Z","response_time":54,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["dicom-viewer","healthcare","image-analysis","medical-imaging","opencv-python","pydicom","pyqt5-gui","qdrant","radiology","sqlite3","vector-databases"],"created_at":"2026-04-08T21:00:59.050Z","updated_at":"2026-04-08T21:01:42.353Z","avatar_url":"https://github.com/LEOSOLAR8.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Medical Imaging Management System with Vector Database\n\n## Project Overview\n\nA comprehensive medical imaging management system. This system combines DICOM image processing with advanced vector database capabilities for efficient storage and retrieval of medical imaging data.\n\n**Key Features**:\n- DICOM image viewer with Hounsfield Unit conversion\n- 28 specialized medical image filters\n- Dual database architecture (Qdrant vector DB + SQLite)\n- Patient/study/series hierarchical organization\n- Radiology report generation\n- Advanced search capabilities\n\n## System Architecture\nproject/\n├── app.py # Main application\n\n├── Filters.py # 28 medical image processing algorithms\n\n├── Qdrant.py # Vector database operations\n\n├── SQLite.py # Relational database operations\n\n├── UI/ # User interface components\n\n│ ├── mainMenuUI.py # Main window\n\n│ ├── editReportUI.py # Report editor\n\n│ └── widgetFinalv2.py # DICOM tag viewer\n\n└── requirements.txt # Dependencies\n\n\n## Key Requirements Implemented\n\n### Database Architecture (from Section 3.1.2)\n- **Vector Database Collection**: \n  - Stores MR images, radiology reports, and patient data\n  - Uses ResNet-50 for image embeddings (384-dimension vectors)\n  - Maintains unique point IDs with non-null constraints\n  - JSON-formatted DICOM metadata in payload\n\n### Core Functionalities (from Section 3.1.1)\n- DICOM image loading and display\n- Patient data association and management\n- Time-based and patient-name search\n- Image acceptance/rejection workflow\n- Report generation and export\n\n### Quality Attributes (from Section 3.2.1)\n- **Usability**: Intuitive PyQt5 interface\n- **Security**: Patient data protection\n- **Maintainability**: Modular design\n- **Performance**: Optimized vector searches\n- **Compatibility**: Works with hospital information systems\n\n## Technologies Used\n\n- **Python**: Primary programming language (v3.8+ recommended)\n- **SQLite**: Relational database for patient metadata storage\n- **Qdrant Vector Database**: High-performance vector similarity search engine for DICOM images\n- **PyDICOM**: Python package for working with DICOM medical imaging files\n- **PyQt5**: Cross-platform GUI toolkit for the user interface\n- **OpenCV**: Medical image processing and transformations\n- **NumPy/SciPy**: Scientific computing for image array operations\n- **Sentence Transformers**: For generating text embeddings of radiology reports\n\n## Installation Guide\n\n### Prerequisites\n- Python 3.8 or higher\n- Docker (for Qdrant)\n- Git (for cloning repository)\n\n### Step-by-Step Setup\n\n1. **Clone the repository**:\n   ```bash\n   git clone https://github.com/yourusername/dicom-viewer.git\n   cd dicom-viewer\n   \n2. **Install Python dependencies**:\n   ```bash\n   pip install -r requirements.txt\n   \n3. **Launch the application**:\n   ```bash\n   python app.py\n\n## Requirements\n\nThis project uses Qdrant for vector database operations. Before running the application, ensure the Qdrant server is running. For Qdrant installation instructions, \u003ca href=\"https://qdrant.tech/documentation/install/\" target=\"_blank\"\u003eyou can click here\u003c/a\u003e.\n\nThe setup above requires Docker. If you cannot use Docker, you can also run the Qdrant server using the \u003ca href=\"https://github.com/qdrant/qdrant/releases\" target=\"_blank\"\u003ehere\u003c/a\u003e.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fleosolar8%2Fdicom-viewer-image-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fleosolar8%2Fdicom-viewer-image-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fleosolar8%2Fdicom-viewer-image-analysis/lists"}