ct_analyzer

CTScanAnalyzer Class

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Overview

The CTScanAnalyzer class provides advanced tools for analyzing CT (Computed Tomography) scan volumes. It supports conversion to Hounsfield Units, tissue segmentation, 3D edge detection, anomaly detection, volume measurements, bone structure analysis, and nodule detection. The class also includes visualization capabilities and methods for creating bone tissue objects.

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Class Definition

class CTScanAnalyzer:
    def __init__(self, image_analyzer):
        """
        Initialize a new CTScanAnalyzer 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.
hu_ranges	Dict[str, Tuple[int, int]]	Hounsfield Unit ranges for different tissues.

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Methods

Initialization

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

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CT Scan Analysis

• analyze_ct_scan(self, image_stack, slice_thickness=1.0) -> Dict[str, Any] Analyzes a CT scan volume using advanced 3D techniques.

  • Parameters:

    • image_stack: Input CT scan volume (3D array or list of 2D slices).
    • slice_thickness: Thickness of each slice in mm.

  • Returns: Dictionary containing comprehensive analysis results.

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Hounsfield Unit Conversion

• convert_to_hounsfield_units(self, volume) -> np.ndarray Converts CT values to Hounsfield Units (HU).

  • Parameters:

    • volume: Input CT volume.

  • Returns: Volume in Hounsfield Units.

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Volume Enhancement

• enhance_ct_volume(self, hu_volume) -> np.ndarray Enhances CT volume using 3D filtering techniques.

  • Parameters:

    • hu_volume: Input volume in Hounsfield Units.

  • Returns: Enhanced volume.

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Tissue Segmentation

• segment_tissues(self, hu_volume) -> Dict[str, np.ndarray] Segments different tissue types based on Hounsfield Unit values.

  • Parameters:

    • hu_volume: Volume in Hounsfield Units.

  • Returns: Dictionary of tissue masks.

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3D Edge Detection

• detect_3d_edges(self, volume) -> np.ndarray Detects edges in 3D using gradient magnitude.

  • Parameters:

    • volume: Input 3D volume.

  • Returns: 3D edge volume.

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3D Anomaly Detection

• detect_3d_anomalies(self, hu_volume, tissue_segmentation) -> List[Dict[str, Any]] Detects anomalies in 3D CT data using machine learning.

  • Parameters:

    • hu_volume: Volume in Hounsfield Units.
    • tissue_segmentation: Tissue segmentation masks.

  • Returns: List of 3D anomaly locations and scores.

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Volume Measurements

• calculate_volume_measurements(self, tissue_segmentation, slice_thickness) -> Dict[str, Dict[str, float]] Calculates volume measurements for different tissues.

  • Parameters:

    • tissue_segmentation: Tissue segmentation masks.
    • slice_thickness: Thickness of each slice in mm.

  • Returns: Dictionary of volume measurements.

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Bone Structure Analysis

• analyze_bone_structure(self, hu_volume, tissue_segmentation) -> Dict[str, Any] Analyzes bone structure and density.

  • Parameters:

    • hu_volume: Volume in Hounsfield Units.
    • tissue_segmentation: Tissue segmentation masks.

  • Returns: Bone analysis results.

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Nodule Detection

• detect_nodules(self, hu_volume, tissue_segmentation) -> List[Dict[str, Any]] Detects potential nodules or masses in the CT scan.

  • Parameters:

    • hu_volume: Volume in Hounsfield Units.
    • tissue_segmentation: Tissue segmentation masks.

  • Returns: List of detected nodules.

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Visualization

• visualize_ct_analysis(self, analysis_results, slice_index=None) Visualizes CT analysis results for a specific slice or middle slice.

  • Parameters:
    • analysis_results: Results from analyze_ct_scan method.
    • slice_index: Index of slice to visualize.

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Bone Tissue Creation

• create_bone_tissue_from_ct(self, hu_volume, tissue_segmentation, tissue_name: str) -> BoneTissue Creates a BoneTissue object from CT scan analysis results.

  • Parameters:

    • hu_volume: Volume in Hounsfield Units.
    • tissue_segmentation: Tissue segmentation masks.
    • tissue_name: Name for the new BoneTissue object.

  • Returns: BoneTissue object.

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Example Usage

# Initialize ImageAnalyzer
image_analyzer = ImageAnalyzer()

# Initialize CTScanAnalyzer
ct_analyzer = CTScanAnalyzer(image_analyzer)

# Load a CT scan image
image = image_analyzer.load_image("path/to/ct_scan.tif")

# Analyze the CT scan
analysis_results = ct_analyzer.analyze_ct_scan(image, slice_thickness=1.0)

# Visualize the CT analysis
ct_analyzer.visualize_ct_analysis(analysis_results)

# Create a bone tissue object from the CT analysis
bone_tissue = ct_analyzer.create_bone_tissue_from_ct(
    analysis_results["hounsfield_volume"],
    analysis_results["tissue_segmentation"],
    "Femur Tissue"
)
print(bone_tissue)

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Dependencies

• numpy: For numerical operations and array handling.
• cv2 (OpenCV): For image processing tasks such as filtering and edge detection.
• scipy: For scientific computing and image processing.
• skimage: For image processing and analysis, including 3D edge detection and volume measurements.
• sklearn.ensemble.IsolationForest: For anomaly detection.
• sklearn.preprocessing.StandardScaler: For feature normalization.
• matplotlib: For visualization.
• biobridge.definitions.tissues.bone.BoneTissue: For creating bone tissue objects.
• biobridge.blocks.cell.Cell: For creating cell 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 CTScanAnalyzer class is designed for advanced CT scan analysis.
• It supports conversion to Hounsfield Units, tissue segmentation, 3D edge detection, anomaly detection, and volume measurements.
• The visualize_ct_analysis method provides a graphical representation of the analysis results.
• The create_bone_tissue_from_ct method creates a BoneTissue object based on the analysis results, which can be used for further biological simulations.