xray_analyzer

XrayAnalyzer Class

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Overview

The XrayAnalyzer class provides tools for analyzing X-ray images, including contrast enhancement, edge detection, image segmentation, anomaly detection, and bone density measurement. It integrates with an ImageAnalyzer for image processing and provides visualization capabilities.

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Class Definition

class XrayAnalyzer:
    def __init__(self, image_analyzer):
        """
        Initialize a new XrayAnalyzer object.
        :param image_analyzer: ImageAnalyzer instance for image processing
        """
        ...

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Attributes

Attribute	Type	Description
image_analyzer	ImageAnalyzer	ImageAnalyzer instance for image processing.

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Methods

Initialization

• __init__(self, image_analyzer) Initializes a new XrayAnalyzer instance with the specified ImageAnalyzer.

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X-ray Analysis

• analyze_xray(self, image) Analyzes an X-ray image using advanced techniques.

  • Parameters:

    • image: Input X-ray image (ImageJ DataArray).

  • Returns: Dictionary containing analysis results, including enhanced image, edges, segmented image, anomalies, and bone density.

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Contrast Enhancement

• enhance_contrast(self, image) Enhances the contrast of the X-ray image using adaptive histogram equalization.

  • Parameters:

    • image: Input grayscale image.

  • Returns: Contrast-enhanced image.

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Edge Detection

• detect_edges(self, image) Detects edges in the X-ray image using Canny edge detection with automatic threshold selection.

  • Parameters:

    • image: Input grayscale image.

  • Returns: Edge image.

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Image Segmentation

• segment_image(self, image) Segments the X-ray image using K-means clustering.

  • Parameters:

    • image: Input grayscale image.

  • Returns: Segmented image.

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Anomaly Detection

• detect_anomalies(self, image, segmented) Detects potential anomalies in the X-ray image using Isolation Forest.

  • Parameters:

    • image: Original grayscale image.
    • segmented: Segmented image.

  • Returns: List of potential anomalies with coordinates and scores.

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Bone Density Measurement

• measure_bone_density(self, image) Measures approximate bone density from the X-ray image using histogram analysis.

  • Parameters:

    • image: Input grayscale image.

  • Returns: Dictionary containing estimated bone density value and histogram.

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Visualization

• visualize_xray_analysis(self, original_image, analysis_results) Visualizes the results of X-ray analysis.

  • Parameters:
    • original_image: Original X-ray image.
    • analysis_results: Results from analyze_xray method.

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Bone Tissue Creation

• create_bone_tissue_from_xray(self, image, tissue_name: str) -> BoneTissue Creates a BoneTissue object from X-ray analysis results.

  • Parameters:

    • image: Input grayscale image.
    • tissue_name: Name for the new BoneTissue object.

  • Returns: BoneTissue object.

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Example Usage

# Initialize ImageAnalyzer
image_analyzer = ImageAnalyzer()

# Initialize XrayAnalyzer
xray_analyzer = XrayAnalyzer(image_analyzer)

# Load an X-ray image
image = image_analyzer.load_image("path/to/xray_image.tif")

# Analyze the X-ray image
analysis_results = xray_analyzer.analyze_xray(image)

# Visualize the X-ray analysis
xray_analyzer.visualize_xray_analysis(image, analysis_results)

# Create a BoneTissue object from the X-ray analysis
bone_tissue = xray_analyzer.create_bone_tissue_from_xray(image, "Femur Tissue")
print(bone_tissue)

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Dependencies

• cv2 (OpenCV): For image processing tasks such as contrast enhancement and edge detection.
• numpy: For numerical operations and array handling.
• sklearn.ensemble.IsolationForest: For anomaly detection.
• sklearn.preprocessing.StandardScaler: For feature normalization.
• matplotlib: For visualization.
• biobridge.blocks.cell.Cell: For creating cell objects.
• biobridge.definitions.tissues.bone.BoneTissue: For creating bone tissue objects.

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Error Handling

• The class includes checks for valid input data and handles potential errors during image processing and analysis.

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Notes

• The XrayAnalyzer class is designed for advanced X-ray image analysis.
• It supports contrast enhancement, edge detection, segmentation, anomaly detection, and bone density measurement.
• The visualize_xray_analysis method provides a graphical representation of the analysis results.
• The create_bone_tissue_from_xray method creates a BoneTissue object based on the analysis results, which can be used for further biological simulations.