# MRIAnalyzer Class

---

## Overview

The `MRIAnalyzer` class provides advanced tools for analyzing MRI images, including bias field correction, noise reduction, contrast enhancement, anatomical structure segmentation, lesion detection, tissue property measurement, texture feature extraction, brain region detection, cortical analysis, subcortical structure segmentation, brain connectivity analysis, and morphometric feature computation. It supports visualization of analysis results and creation of specialized brain tissue objects.

---

## Class Definition

```python
class MRIAnalyzer:
    def __init__(self, image_analyzer):
        """
        Initialize a new MRIAnalyzer object.
        :param image_analyzer: ImageAnalyzer instance for image processing
        """
        self.image_analyzer = image_analyzer
        self.atlas_templates = self._initialize_brain_atlas()
        self.connectivity_matrix = None
        self.cortical_thickness_map = None
```

---

## Attributes

| Attribute | Type | Description |
|-----------|------|-------------|
| `image_analyzer` | `ImageAnalyzer` | ImageAnalyzer instance for image processing. |
| `atlas_templates` | `Dict[str, Any]` | Brain atlas templates for anatomical labeling. |
| `connectivity_matrix` | `np.ndarray` | Matrix representing brain connectivity. |
| `cortical_thickness_map` | `np.ndarray` | Map of cortical thickness values. |

---

## Methods

### Initialization

- **`__init__(self, image_analyzer)`**
  Initializes a new `MRIAnalyzer` instance with the specified `ImageAnalyzer`.

- **`_initialize_brain_atlas(self) -> Dict[str, Any]`**
  Initializes brain atlas templates for anatomical labeling.
  - **Returns**: Dictionary containing Brodmann areas and subcortical structures.

---

### MRI Analysis

- **`analyze_mri(self, image, sequence_type="T1") -> Dict[str, Any]`**
  Analyzes an MRI image using advanced techniques.
  - **Parameters**:
    - `image`: Input MRI image (ImageJ DataArray).
    - `sequence_type`: MRI sequence type ("T1", "T2", "FLAIR", "DWI", etc.).
  - **Returns**: Dictionary containing analysis results, including bias-corrected image, denoised image, enhanced image, segmented image, lesions, tissue properties, texture features, and brain regions.

---

### Bias Field Correction

- **`correct_bias_field(self, image) -> np.ndarray`**
  Corrects bias field inhomogeneity in MRI images using Gaussian blur and morphological operations.
  - **Parameters**:
    - `image`: Input grayscale image.
  - **Returns**: Bias field corrected image.

---

### Noise Reduction

- **`reduce_noise(self, image) -> np.ndarray`**
  Reduces noise in MRI images using Non-local Means Denoising.
  - **Parameters**:
    - `image`: Input grayscale image.
  - **Returns**: Denoised image.

---

### Contrast Enhancement

- **`enhance_contrast(self, image, sequence_type) -> np.ndarray`**
  Enhances contrast based on MRI sequence type.
  - **Parameters**:
    - `image`: Input grayscale image.
    - `sequence_type`: MRI sequence type.
  - **Returns**: Contrast-enhanced image.

---

### Anatomical Structure Segmentation

- **`segment_anatomical_structures(self, image, sequence_type) -> np.ndarray`**
  Segments anatomical structures in MRI images using multi-level thresholding and clustering.
  - **Parameters**:
    - `image`: Input grayscale image.
    - `sequence_type`: MRI sequence type.
  - **Returns**: Segmented image with labeled regions.

---

### Lesion Detection

- **`detect_lesions(self, image, sequence_type) -> List[Dict[str, Any]]`**
  Detects potential lesions or abnormalities in MRI images.
  - **Parameters**:
    - `image`: Input grayscale image.
    - `sequence_type`: MRI sequence type.
  - **Returns**: List of detected lesions with properties.

---

### Tissue Property Measurement

- **`measure_tissue_properties(self, image, segmented, sequence_type) -> Dict[str, Dict[str, float]]`**
  Measures tissue properties from segmented MRI images.
  - **Parameters**:
    - `image`: Original grayscale image.
    - `segmented`: Segmented image.
    - `sequence_type`: MRI sequence type.
  - **Returns**: Dictionary of tissue properties.

- **`_get_tissue_name(self, label, sequence_type) -> str`**
  Maps cluster labels to tissue names based on sequence type.
  - **Parameters**:
    - `label`: Cluster label.
    - `sequence_type`: MRI sequence type.
  - **Returns**: Tissue name.

---

### Texture Feature Extraction

- **`extract_texture_features(self, image) -> Dict[str, float]`**
  Extracts texture features from MRI images using gradient and LBP features.
  - **Parameters**:
    - `image`: Input grayscale image.
  - **Returns**: Dictionary of texture features.

---

### Brain Region Detection

- **`detect_brain_regions(self, image) -> Dict[str, Any]`**
  Detects and classifies brain regions in MRI images.
  - **Parameters**:
    - `image`: Input grayscale image.
  - **Returns**: Dictionary of detected brain regions, including cortical analysis, subcortical structures, connectivity metrics, and morphometric features.

- **`_extract_brain_mask(self, image) -> np.ndarray`**
  Extracts brain mask (simplified skull stripping).
  - **Parameters**:
    - `image`: Input grayscale image.
  - **Returns**: Binary brain mask.

- **`_perform_brain_segmentation(self, brain_image) -> Dict[str, Any]`**
  Performs brain segmentation using multi-scale features and clustering.
  - **Parameters**:
    - `brain_image`: Brain image.
  - **Returns**: Dictionary of segmented brain regions.

- **`_extract_multiscale_features(self, image) -> Dict[str, np.ndarray]`**
  Extracts multi-scale features from the brain image.
  - **Parameters**:
    - `image`: Input grayscale image.
  - **Returns**: Dictionary of multi-scale features.

- **`_refine_segmentation_with_morphology(self, segmentation) -> np.ndarray`**
  Refines segmentation using morphological operations.
  - **Parameters**:
    - `segmentation`: Initial segmentation.
  - **Returns**: Refined segmentation.

- **`_map_clusters_to_anatomical_regions(self, clustered_image, brain_image) -> Dict[str, Any]`**
  Maps clusters to anatomical regions.
  - **Parameters**:
    - `clustered_image`: Clustered image.
    - `brain_image`: Brain image.
  - **Returns**: Dictionary of anatomical regions.

- **`_assign_anatomical_label(self, centroid, image_shape, intensity_data) -> str`**
  Assigns anatomical labels based on centroid location and intensity.
  - **Parameters**:
    - `centroid`: Centroid coordinates.
    - `image_shape`: Shape of the image.
    - `intensity_data`: Intensity data.
  - **Returns**: Anatomical label.

---

### Cortical Analysis

- **`_analyze_cortical_structure(self, brain_image, brain_mask) -> Dict[str, Any]`**
  Analyzes cortical structure, including thickness, curvature, and surface area.
  - **Parameters**:
    - `brain_image`: Brain image.
    - `brain_mask`: Brain mask.
  - **Returns**: Dictionary of cortical analysis results.

- **`_segment_cortical_areas(self, brain_image, brain_mask) -> Dict[str, Any]`**
  Segments cortical areas into sectors.
  - **Parameters**:
    - `brain_image`: Brain image.
    - `brain_mask`: Brain mask.
  - **Returns**: Dictionary of cortical regions.

- **`_create_sectorial_mask(self, brain_mask, center, angle, sector_width) -> np.ndarray`**
  Creates a sectorial mask for cortical analysis.
  - **Parameters**:
    - `brain_mask`: Brain mask.
    - `center`: Center coordinates.
    - `angle`: Sector angle.
    - `sector_width`: Sector width.
  - **Returns**: Sectorial mask.

- **`_compute_cortical_thickness(self, brain_image, brain_mask) -> np.ndarray`**
  Computes cortical thickness.
  - **Parameters**:
    - `brain_image`: Brain image.
    - `brain_mask`: Brain mask.
  - **Returns**: Cortical thickness map.

- **`_analyze_cortical_curvature(self, brain_image, brain_mask) -> Dict[str, Any]`**
  Analyzes cortical curvature.
  - **Parameters**:
    - `brain_image`: Brain image.
    - `brain_mask`: Brain mask.
  - **Returns**: Dictionary of curvature metrics.

- **`_estimate_cortical_surface_area(self, brain_mask) -> Dict[str, Any]`**
  Estimates cortical surface area.
  - **Parameters**:
    - `brain_mask`: Brain mask.
  - **Returns**: Dictionary of surface area metrics.

- **`_compute_fractal_dimension(self, contour) -> float`**
  Computes fractal dimension of a contour.
  - **Parameters**:
    - `contour`: Contour.
  - **Returns**: Fractal dimension.

- **`_analyze_gyral_sulcal_pattern(self, brain_image, brain_mask) -> Dict[str, Any]`**
  Analyzes gyral and sulcal patterns.
  - **Parameters**:
    - `brain_image`: Brain image.
    - `brain_mask`: Brain mask.
  - **Returns**: Dictionary of gyral and sulcal patterns.

---

### Subcortical Analysis

- **`_segment_subcortical_structures(self, brain_image, brain_mask) -> Dict[str, Any]`**
  Segments subcortical structures.
  - **Parameters**:
    - `brain_image`: Brain image.
    - `brain_mask`: Brain mask.
  - **Returns**: Dictionary of subcortical structures.

- **`_identify_deep_brain_structures(self, brain_image, brain_mask) -> Dict[str, Any]`**
  Identifies deep brain structures.
  - **Parameters**:
    - `brain_image`: Brain image.
    - `brain_mask`: Brain mask.
  - **Returns**: Dictionary of deep brain structures.

- **`_analyze_ventricular_system(self, brain_image, brain_mask) -> Dict[str, Any]`**
  Analyzes the ventricular system.
  - **Parameters**:
    - `brain_image`: Brain image.
    - `brain_mask`: Brain mask.
  - **Returns**: Dictionary of ventricular analysis.

- **`_analyze_white_matter_integrity(self, brain_image, brain_mask) -> Dict[str, Any]`**
  Analyzes white matter integrity.
  - **Parameters**:
    - `brain_image`: Brain image.
    - `brain_mask`: Brain mask.
  - **Returns**: Dictionary of white matter integrity metrics.

- **`_compute_tract_coherence(self, wm_mask) -> float`**
  Computes tract coherence.
  - **Parameters**:
    - `wm_mask`: White matter mask.
  - **Returns**: Tract coherence score.

---

### Brain Connectivity Analysis

- **`_analyze_brain_connectivity(self, brain_image, brain_mask) -> Dict[str, Any]`**
  Analyzes brain connectivity.
  - **Parameters**:
    - `brain_image`: Brain image.
    - `brain_mask`: Brain mask.
  - **Returns**: Dictionary of connectivity metrics.

- **`_compute_structural_connectivity(self, brain_image, brain_mask) -> np.ndarray`**
  Computes structural connectivity matrix.
  - **Parameters**:
    - `brain_image`: Brain image.
    - `brain_mask`: Brain mask.
  - **Returns**: Connectivity matrix.

- **`_analyze_network_topology(self, connectivity_matrix) -> Dict[str, Any]`**
  Analyzes network topology.
  - **Parameters**:
    - `connectivity_matrix`: Connectivity matrix.
  - **Returns**: Dictionary of network metrics.

- **`_compute_clustering_coefficient(self, binary_matrix) -> float`**
  Computes clustering coefficient.
  - **Parameters**:
    - `binary_matrix`: Binary matrix.
  - **Returns**: Clustering coefficient.

- **`_compute_characteristic_path_length(self, binary_matrix) -> float`**
  Computes characteristic path length.
  - **Parameters**:
    - `binary_matrix`: Binary matrix.
  - **Returns**: Characteristic path length.

- **`_compute_small_worldness(self, clustering_coeff, path_length) -> float`**
  Computes small-worldness.
  - **Parameters**:
    - `clustering_coeff`: Clustering coefficient.
    - `path_length`: Path length.
  - **Returns**: Small-worldness score.

- **`_identify_connectivity_hubs(self, connectivity_matrix) -> List[Dict[str, Any]]`**
  Identifies connectivity hubs.
  - **Parameters**:
    - `connectivity_matrix`: Connectivity matrix.
  - **Returns**: List of connectivity hubs.

- **`_compute_betweenness_centrality(self, connectivity_matrix) -> np.ndarray`**
  Computes betweenness centrality.
  - **Parameters**:
    - `connectivity_matrix`: Connectivity matrix.
  - **Returns**: Betweenness centrality scores.

- **`_detect_communities(self, connectivity_matrix) -> np.ndarray`**
  Detects communities in the connectivity matrix.
  - **Parameters**:
    - `connectivity_matrix`: Connectivity matrix.
  - **Returns**: Community labels.

- **`_find_connected_component(self, adjacency_matrix, start_node) -> List[int]`**
  Finds connected components.
  - **Parameters**:
    - `adjacency_matrix`: Adjacency matrix.
    - `start_node`: Start node.
  - **Returns**: List of connected nodes.

- **`_compute_global_efficiency(self, connectivity_matrix) -> float`**
  Computes global efficiency.
  - **Parameters**:
    - `connectivity_matrix`: Connectivity matrix.
  - **Returns**: Global efficiency score.

- **`_compute_modularity(self, connectivity_matrix) -> float`**
  Computes modularity.
  - **Parameters**:
    - `connectivity_matrix`: Connectivity matrix.
  - **Returns**: Modularity score.

---

### Brain Morphometry

- **`_compute_brain_morphometry(self, brain_image, brain_mask) -> Dict[str, Any]`**
  Computes brain morphometry.
  - **Parameters**:
    - `brain_image`: Brain image.
    - `brain_mask`: Brain mask.
  - **Returns**: Dictionary of morphometric features.

- **`_compute_volumetric_measures(self, brain_mask) -> Dict[str, Any]`**
  Computes volumetric measures.
  - **Parameters**:
    - `brain_mask`: Brain mask.
  - **Returns**: Dictionary of volumetric measures.

- **`_analyze_brain_asymmetry(self, brain_image, brain_mask) -> Dict[str, Any]`**
  Analyzes brain asymmetry.
  - **Parameters**:
    - `brain_image`: Brain image.
    - `brain_mask`: Brain mask.
  - **Returns**: Dictionary of asymmetry metrics.

- **`_analyze_brain_shape(self, brain_mask) -> Dict[str, Any]`**
  Analyzes brain shape.
  - **Parameters**:
    - `brain_mask`: Brain mask.
  - **Returns**: Dictionary of shape metrics.

- **`_compute_advanced_texture_measures(self, brain_image, brain_mask) -> Dict[str, Any]`**
  Computes advanced texture measures.
  - **Parameters**:
    - `brain_image`: Brain image.
    - `brain_mask`: Brain mask.
  - **Returns**: Dictionary of texture complexity metrics.

- **`_compute_wavelet_features(self, brain_image, brain_mask) -> Dict[str, Any]`**
  Computes wavelet features.
  - **Parameters**:
    - `brain_image`: Brain image.
    - `brain_mask`: Brain mask.
  - **Returns**: Dictionary of wavelet features.

---

### Visualization

- **`visualize_mri_analysis(self, original_image, analysis_results)`**
  Visualizes the results of MRI analysis.
  - **Parameters**:
    - `original_image`: Original MRI image.
    - `analysis_results`: Results from `analyze_mri` method.

---

### Brain Tissue Creation

- **`create_brain_tissue_from_mri(self, image, tissue_name: str, sequence_type="T1") -> RadiationAffectedTissue`**
  Creates a specialized brain tissue object from MRI analysis results.
  - **Parameters**:
    - `image`: Input grayscale MRI image.
    - `tissue_name`: Name for the new tissue object.
    - `sequence_type`: MRI sequence type.
  - **Returns**: `RadiationAffectedTissue` object.

---

### MRI Sequence Comparison

- **`compare_mri_sequences(self, t1_image, t2_image, flair_image, tissue_name: str) -> Dict[str, Any]`**
  Compares analysis results from different MRI sequences.
  - **Parameters**:
    - `t1_image`: T1-weighted MRI image.
    - `t2_image`: T2-weighted MRI image.
    - `flair_image`: FLAIR MRI image.
    - `tissue_name`: Name for the tissue analysis.
  - **Returns**: Dictionary containing comparison results.

---

## Example Usage

```python
# Initialize ImageAnalyzer
image_analyzer = ImageAnalyzer()

# Initialize MRIAnalyzer
mri_analyzer = MRIAnalyzer(image_analyzer)

# Load an MRI image
image = image_analyzer.load_image("path/to/mri_image.tif")

# Analyze the MRI image
analysis_results = mri_analyzer.analyze_mri(image, sequence_type="T1")

# Visualize the MRI analysis
mri_analyzer.visualize_mri_analysis(image, analysis_results)

# Create a brain tissue object from the MRI analysis
brain_tissue = mri_analyzer.create_brain_tissue_from_mri(image, "Brain Tissue", "T1")
print(brain_tissue)

# Compare MRI sequences
t1_image = image_analyzer.load_image("path/to/t1_image.tif")
t2_image = image_analyzer.load_image("path/to/t2_image.tif")
flair_image = image_analyzer.load_image("path/to/flair_image.tif")
comparison_results = mri_analyzer.compare_mri_sequences(t1_image, t2_image, flair_image, "Brain Tissue")
print(comparison_results)
```

---

## Dependencies

- **`cv2` (OpenCV)**: For image processing tasks such as contrast enhancement, edge detection, and noise reduction.
- **`numpy`**: For numerical operations and array handling.
- **`scipy`**: For scientific computing and image processing.
- **`scipy.ndimage`**: For morphological operations and distance transforms.
- **`skimage`**: For image processing and analysis.
- **`sklearn.cluster.KMeans`**: For clustering-based segmentation.
- **`sklearn.preprocessing.StandardScaler`**: For feature normalization.
- **`matplotlib`**: For visualization.
- **`biobridge.blocks.cell.Cell`**: For creating cell objects.
- **`biobridge.definitions.tissues.radiation_affected.RadiationAffectedTissue`**: For creating specialized brain tissue objects.

---

## Error Handling

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

---

## Notes

- The `MRIAnalyzer` class is designed for advanced MRI image analysis.
- It supports multiple MRI sequence types and provides detailed analysis of anatomical structures, lesions, tissue properties, texture features, brain regions, cortical analysis, subcortical structures, brain connectivity, and morphometric features.
- The `visualize_mri_analysis` method provides a graphical representation of the analysis results.
- The `create_brain_tissue_from_mri` method creates specialized brain tissue objects based on the analysis results, which can be used for further biological simulations.
- The `compare_mri_sequences` method allows for comparative analysis of different MRI sequences, providing clinical insights.
