# Panoramic X-ray Tooth Dataset

## Description
This dataset contains over 500 panoramic dental X-ray images, accompanied by segmentation masks and annotation files. The dataset aims to support dental research, machine learning model development, and automated dental diagnostics. The images in this dataset represent a variety of dental conditions and tooth structures.

## Dataset Structure
The dataset includes the following components:

1. **X-ray Images**: High-resolution panoramic X-ray images in JPG format.
2. **Machine Masks**: Segmentation masks automatically generated by machine learning models, representing the detected teeth and surrounding structures.
3. **Human Masks**: Segmentation masks manually annotated by dental professionals, serving as ground truth.
4. **JSON Annotation Files**: Each X-ray image is accompanied by a JSON file containing polygon coordinates outlining the exterior boundaries of each tooth, along with class labels.

### Example Files
- **1.jpg**: Example panoramic X-ray image. 
  - **Explanation**: This image is a standard panoramic X-ray, capturing the full set of teeth, jaw, and surrounding bone structure in a single image. The process involves using a rotating arm of an X-ray machine that captures the dental arch. This type of imaging is widely used for dental diagnosis, as it provides a comprehensive view of the mouth.
  - **Usage**: This image can be used to detect dental anomalies, evaluate tooth alignment, and check for issues such as impacted teeth or bone loss.

- **1.png**: Corresponding machine or human mask.
  - **Explanation**: The mask highlights individual teeth by segmenting them from the X-ray image. In the case of machine-generated masks, computer vision models have been trained to detect and separate each tooth, while human masks are manually drawn for accuracy.
  - **Usage**: Overlay the mask on the X-ray to visualize segmentation. This is useful for training machine learning models to improve automatic segmentation accuracy.

- **1.jpg.json**: Annotation file containing labeled polygon data for each tooth.
  - **Explanation**: This JSON file contains detailed polygon coordinates that map out the boundaries of each tooth detected in the X-ray. Each entry in the file corresponds to a specific tooth number or region, providing crucial information for dental diagnostics and AI model training.
  - **Usage**: The polygon data can be used to train segmentation models, fine-tune existing algorithms, or serve as ground truth for evaluating model performance.

## JSON File Breakdown
Each JSON file includes:
- **Objects**: A list of segmented teeth, each represented by a polygon.
- **ClassTitle**: Labels representing tooth numbers.
- **Points**: Polygon coordinates marking the exterior of each detected tooth.
- **Size**: Width and height of the original image.

**Example Entry:**
```json
{
    "classTitle": "8",
    "points": {
        "exterior": [[963, 587], [964, 569], [968, 546], ...],
        "interior": []
    }
}
```

## Usage
This dataset can be used to train segmentation models for tooth identification, dental health assessment, and automated diagnostics. The machine-generated masks can help in benchmarking, while human-annotated masks provide reliable ground truth for comparison.

### Application Workflow
1. **Load the X-ray Images**: Use the images for preprocessing and augmentation.
2. **Apply the Masks**: Overlay machine or human masks to visualize tooth segmentation.
3. **Model Training**: Utilize the masks and annotations to train segmentation models.
4. **Evaluation**: Compare model output with human annotations for accuracy assessment.

## Author and Attribution
- **Annotations by**: GhazalehHITL
- **Dataset Curator**: [Your Name or Profile Link]
- **Source**: This dataset is publicly available for non-commercial research and development.

## Download
Click the link below to download the dataset:
[Download Dataset](#)

## License
This dataset is licensed for educational and research purposes. Proper attribution is required for any publications or projects utilizing this dataset.

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If you have any questions or require further information, feel free to reach out via [Your Contact Information].