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+# Visualization
+
+## Overview
+
+Histolab provides several built-in visualization methods to help inspect slides, preview tile locations, visualize masks, and assess extraction quality. Proper visualization is essential for validating preprocessing pipelines, debugging extraction issues, and presenting results.
+
+## Slide Visualization
+
+### Thumbnail Display
+
+```python
+from histolab.slide import Slide
+import matplotlib.pyplot as plt
+
+slide = Slide("slide.svs", processed_path="output/")
+
+# Display thumbnail
+plt.figure(figsize=(10, 10))
+plt.imshow(slide.thumbnail)
+plt.title(f"Slide: {slide.name}")
+plt.axis('off')
+plt.show()
+```
+
+### Save Thumbnail to Disk
+
+```python
+# Save thumbnail as image file
+slide.save_thumbnail()
+# Saves to processed_path/thumbnails/slide_name_thumb.png
+```
+
+### Scaled Images
+
+```python
+# Get scaled version of slide at specific downsample factor
+scaled_img = slide.scaled_image(scale_factor=32)
+
+plt.imshow(scaled_img)
+plt.title(f"Slide at 32x downsample")
+plt.show()
+```
+
+## Mask Visualization
+
+### Using locate_mask()
+
+```python
+from histolab.masks import TissueMask, BiggestTissueBoxMask
+
+# Visualize TissueMask
+tissue_mask = TissueMask()
+slide.locate_mask(tissue_mask)
+
+# Visualize BiggestTissueBoxMask
+biggest_mask = BiggestTissueBoxMask()
+slide.locate_mask(biggest_mask)
+```
+
+This displays the slide thumbnail with mask boundaries overlaid in red.
+
+### Manual Mask Visualization
+
+```python
+import matplotlib.pyplot as plt
+from histolab.masks import TissueMask
+
+slide = Slide("slide.svs", processed_path="output/")
+mask = TissueMask()
+
+# Generate mask
+mask_array = mask(slide)
+
+# Create side-by-side comparison
+fig, axes = plt.subplots(1, 3, figsize=(20, 7))
+
+# Original thumbnail
+axes[0].imshow(slide.thumbnail)
+axes[0].set_title("Original Slide")
+axes[0].axis('off')
+
+# Binary mask
+axes[1].imshow(mask_array, cmap='gray')
+axes[1].set_title("Tissue Mask")
+axes[1].axis('off')
+
+# Overlay mask on thumbnail
+from matplotlib.colors import ListedColormap
+overlay = slide.thumbnail.copy()
+axes[2].imshow(overlay)
+axes[2].imshow(mask_array, cmap=ListedColormap(['none', 'red']), alpha=0.3)
+axes[2].set_title("Mask Overlay")
+axes[2].axis('off')
+
+plt.tight_layout()
+plt.show()
+```
+
+### Comparing Multiple Masks
+
+```python
+from histolab.masks import TissueMask, BiggestTissueBoxMask
+
+masks = {
+    'TissueMask': TissueMask(),
+    'BiggestTissueBoxMask': BiggestTissueBoxMask()
+}
+
+fig, axes = plt.subplots(1, len(masks) + 1, figsize=(20, 6))
+
+# Original
+axes[0].imshow(slide.thumbnail)
+axes[0].set_title("Original")
+axes[0].axis('off')
+
+# Each mask
+for idx, (name, mask) in enumerate(masks.items(), 1):
+    mask_array = mask(slide)
+    axes[idx].imshow(mask_array, cmap='gray')
+    axes[idx].set_title(name)
+    axes[idx].axis('off')
+
+plt.tight_layout()
+plt.show()
+```
+
+## Tile Location Preview
+
+### Using locate_tiles()
+
+Preview tile locations before extraction:
+
+```python
+from histolab.tiler import RandomTiler, GridTiler, ScoreTiler
+from histolab.scorer import NucleiScorer
+
+# RandomTiler preview
+random_tiler = RandomTiler(
+    tile_size=(512, 512),
+    n_tiles=50,
+    level=0,
+    seed=42
+)
+random_tiler.locate_tiles(slide, n_tiles=20)
+
+# GridTiler preview
+grid_tiler = GridTiler(
+    tile_size=(512, 512),
+    level=0
+)
+grid_tiler.locate_tiles(slide)
+
+# ScoreTiler preview
+score_tiler = ScoreTiler(
+    tile_size=(512, 512),
+    n_tiles=30,
+    scorer=NucleiScorer()
+)
+score_tiler.locate_tiles(slide, n_tiles=15)
+```
+
+This displays colored rectangles on the slide thumbnail indicating where tiles will be extracted.
+
+### Custom Tile Location Visualization
+
+```python
+import matplotlib.pyplot as plt
+import matplotlib.patches as patches
+from histolab.tiler import RandomTiler
+
+slide = Slide("slide.svs", processed_path="output/")
+tiler = RandomTiler(tile_size=(512, 512), n_tiles=30, seed=42)
+
+# Get thumbnail and scale factor
+thumbnail = slide.thumbnail
+scale_factor = slide.dimensions[0] / thumbnail.size[0]
+
+# Generate tile coordinates (without extracting)
+fig, ax = plt.subplots(figsize=(12, 12))
+ax.imshow(thumbnail)
+ax.set_title("Tile Locations Preview")
+ax.axis('off')
+
+# Manually add rectangles for each tile location
+# Note: This is conceptual - actual implementation would retrieve coordinates from tiler
+tile_coords = []  # Would be populated by tiler logic
+for coord in tile_coords:
+    x, y = coord[0] / scale_factor, coord[1] / scale_factor
+    w, h = 512 / scale_factor, 512 / scale_factor
+    rect = patches.Rectangle((x, y), w, h,
+                             linewidth=2, edgecolor='red',
+                             facecolor='none')
+    ax.add_patch(rect)
+
+plt.show()
+```
+
+## Tile Visualization
+
+### Display Extracted Tiles
+
+```python
+from pathlib import Path
+from PIL import Image
+import matplotlib.pyplot as plt
+
+tile_dir = Path("output/tiles/")
+tile_paths = list(tile_dir.glob("*.png"))[:16]  # First 16 tiles
+
+fig, axes = plt.subplots(4, 4, figsize=(12, 12))
+axes = axes.ravel()
+
+for idx, tile_path in enumerate(tile_paths):
+    tile_img = Image.open(tile_path)
+    axes[idx].imshow(tile_img)
+    axes[idx].set_title(tile_path.stem, fontsize=8)
+    axes[idx].axis('off')
+
+plt.tight_layout()
+plt.show()
+```
+
+### Tile Grid Mosaic
+
+```python
+def create_tile_mosaic(tile_dir, grid_size=(4, 4)):
+    """Create mosaic of tiles."""
+    tile_paths = list(Path(tile_dir).glob("*.png"))[:grid_size[0] * grid_size[1]]
+
+    fig, axes = plt.subplots(grid_size[0], grid_size[1], figsize=(16, 16))
+
+    for idx, tile_path in enumerate(tile_paths):
+        row = idx // grid_size[1]
+        col = idx % grid_size[1]
+        tile_img = Image.open(tile_path)
+        axes[row, col].imshow(tile_img)
+        axes[row, col].axis('off')
+
+    plt.tight_layout()
+    plt.savefig("tile_mosaic.png", dpi=150, bbox_inches='tight')
+    plt.show()
+
+create_tile_mosaic("output/tiles/", grid_size=(5, 5))
+```
+
+### Tile with Tissue Mask Overlay
+
+```python
+from histolab.tile import Tile
+import matplotlib.pyplot as plt
+
+# Assume we have a tile object
+tile = Tile(image=pil_image, coords=(x, y))
+
+# Calculate tissue mask
+tile.calculate_tissue_mask()
+
+fig, axes = plt.subplots(1, 3, figsize=(15, 5))
+
+# Original tile
+axes[0].imshow(tile.image)
+axes[0].set_title("Original Tile")
+axes[0].axis('off')
+
+# Tissue mask
+axes[1].imshow(tile.tissue_mask, cmap='gray')
+axes[1].set_title(f"Tissue Mask ({tile.tissue_ratio:.1%} tissue)")
+axes[1].axis('off')
+
+# Overlay
+axes[2].imshow(tile.image)
+axes[2].imshow(tile.tissue_mask, cmap='Reds', alpha=0.3)
+axes[2].set_title("Overlay")
+axes[2].axis('off')
+
+plt.tight_layout()
+plt.show()
+```
+
+## Quality Assessment Visualization
+
+### Tile Score Distribution
+
+```python
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+# Load tile report from ScoreTiler
+report_df = pd.read_csv("tiles_report.csv")
+
+# Score distribution histogram
+plt.figure(figsize=(10, 6))
+plt.hist(report_df['score'], bins=30, edgecolor='black', alpha=0.7)
+plt.xlabel('Tile Score')
+plt.ylabel('Frequency')
+plt.title('Distribution of Tile Scores')
+plt.grid(axis='y', alpha=0.3)
+plt.show()
+
+# Score vs tissue percentage scatter
+plt.figure(figsize=(10, 6))
+plt.scatter(report_df['tissue_percent'], report_df['score'], alpha=0.5)
+plt.xlabel('Tissue Percentage')
+plt.ylabel('Tile Score')
+plt.title('Tile Score vs Tissue Coverage')
+plt.grid(alpha=0.3)
+plt.show()
+```
+
+### Top vs Bottom Scoring Tiles
+
+```python
+import pandas as pd
+from PIL import Image
+import matplotlib.pyplot as plt
+
+# Load tile report
+report_df = pd.read_csv("tiles_report.csv")
+report_df = report_df.sort_values('score', ascending=False)
+
+# Top 8 tiles
+top_tiles = report_df.head(8)
+# Bottom 8 tiles
+bottom_tiles = report_df.tail(8)
+
+fig, axes = plt.subplots(2, 8, figsize=(20, 6))
+
+# Display top tiles
+for idx, (_, row) in enumerate(top_tiles.iterrows()):
+    tile_img = Image.open(f"output/tiles/{row['tile_name']}")
+    axes[0, idx].imshow(tile_img)
+    axes[0, idx].set_title(f"Score: {row['score']:.3f}", fontsize=8)
+    axes[0, idx].axis('off')
+
+# Display bottom tiles
+for idx, (_, row) in enumerate(bottom_tiles.iterrows()):
+    tile_img = Image.open(f"output/tiles/{row['tile_name']}")
+    axes[1, idx].imshow(tile_img)
+    axes[1, idx].set_title(f"Score: {row['score']:.3f}", fontsize=8)
+    axes[1, idx].axis('off')
+
+axes[0, 0].set_ylabel('Top Scoring', fontsize=12)
+axes[1, 0].set_ylabel('Bottom Scoring', fontsize=12)
+
+plt.tight_layout()
+plt.savefig("score_comparison.png", dpi=150, bbox_inches='tight')
+plt.show()
+```
+
+## Multi-Slide Visualization
+
+### Slide Collection Thumbnails
+
+```python
+from pathlib import Path
+from histolab.slide import Slide
+import matplotlib.pyplot as plt
+
+slide_dir = Path("slides/")
+slide_paths = list(slide_dir.glob("*.svs"))[:9]
+
+fig, axes = plt.subplots(3, 3, figsize=(15, 15))
+axes = axes.ravel()
+
+for idx, slide_path in enumerate(slide_paths):
+    slide = Slide(slide_path, processed_path="output/")
+    axes[idx].imshow(slide.thumbnail)
+    axes[idx].set_title(slide.name, fontsize=10)
+    axes[idx].axis('off')
+
+plt.tight_layout()
+plt.savefig("slide_collection.png", dpi=150, bbox_inches='tight')
+plt.show()
+```
+
+### Tissue Coverage Comparison
+
+```python
+from pathlib import Path
+from histolab.slide import Slide
+from histolab.masks import TissueMask
+import matplotlib.pyplot as plt
+import numpy as np
+
+slide_paths = list(Path("slides/").glob("*.svs"))
+tissue_percentages = []
+slide_names = []
+
+for slide_path in slide_paths:
+    slide = Slide(slide_path, processed_path="output/")
+    mask = TissueMask()(slide)
+    tissue_pct = mask.sum() / mask.size * 100
+    tissue_percentages.append(tissue_pct)
+    slide_names.append(slide.name)
+
+# Bar plot
+plt.figure(figsize=(12, 6))
+plt.bar(range(len(slide_names)), tissue_percentages)
+plt.xticks(range(len(slide_names)), slide_names, rotation=45, ha='right')
+plt.ylabel('Tissue Coverage (%)')
+plt.title('Tissue Coverage Across Slides')
+plt.grid(axis='y', alpha=0.3)
+plt.tight_layout()
+plt.show()
+```
+
+## Filter Effect Visualization
+
+### Before and After Filtering
+
+```python
+from histolab.filters.image_filters import RgbToGrayscale, HistogramEqualization
+from histolab.filters.compositions import Compose
+
+# Define filter pipeline
+filter_pipeline = Compose([
+    RgbToGrayscale(),
+    HistogramEqualization()
+])
+
+# Original vs filtered
+fig, axes = plt.subplots(1, 2, figsize=(12, 6))
+
+axes[0].imshow(slide.thumbnail)
+axes[0].set_title("Original")
+axes[0].axis('off')
+
+filtered = filter_pipeline(slide.thumbnail)
+axes[1].imshow(filtered, cmap='gray')
+axes[1].set_title("After Filtering")
+axes[1].axis('off')
+
+plt.tight_layout()
+plt.show()
+```
+
+### Multi-Step Filter Visualization
+
+```python
+from histolab.filters.image_filters import RgbToGrayscale, OtsuThreshold
+from histolab.filters.morphological_filters import BinaryDilation, RemoveSmallObjects
+
+# Individual filter steps
+steps = [
+    ("Original", None),
+    ("Grayscale", RgbToGrayscale()),
+    ("Otsu Threshold", Compose([RgbToGrayscale(), OtsuThreshold()])),
+    ("After Dilation", Compose([RgbToGrayscale(), OtsuThreshold(), BinaryDilation(disk_size=5)])),
+    ("Remove Small Objects", Compose([RgbToGrayscale(), OtsuThreshold(), BinaryDilation(disk_size=5), RemoveSmallObjects(area_threshold=500)]))
+]
+
+fig, axes = plt.subplots(1, len(steps), figsize=(20, 4))
+
+for idx, (title, filter_fn) in enumerate(steps):
+    if filter_fn is None:
+        axes[idx].imshow(slide.thumbnail)
+    else:
+        result = filter_fn(slide.thumbnail)
+        axes[idx].imshow(result, cmap='gray')
+    axes[idx].set_title(title, fontsize=10)
+    axes[idx].axis('off')
+
+plt.tight_layout()
+plt.show()
+```
+
+## Exporting Visualizations
+
+### High-Resolution Exports
+
+```python
+# Export high-resolution figure
+fig, ax = plt.subplots(figsize=(20, 20))
+ax.imshow(slide.thumbnail)
+ax.axis('off')
+plt.savefig("slide_high_res.png", dpi=300, bbox_inches='tight', pad_inches=0)
+plt.close()
+```
+
+### PDF Reports
+
+```python
+from matplotlib.backends.backend_pdf import PdfPages
+
+# Create multi-page PDF report
+with PdfPages('slide_report.pdf') as pdf:
+    # Page 1: Slide thumbnail
+    fig1, ax1 = plt.subplots(figsize=(10, 10))
+    ax1.imshow(slide.thumbnail)
+    ax1.set_title(f"Slide: {slide.name}")
+    ax1.axis('off')
+    pdf.savefig(fig1, bbox_inches='tight')
+    plt.close()
+
+    # Page 2: Tissue mask
+    fig2, ax2 = plt.subplots(figsize=(10, 10))
+    mask = TissueMask()(slide)
+    ax2.imshow(mask, cmap='gray')
+    ax2.set_title("Tissue Mask")
+    ax2.axis('off')
+    pdf.savefig(fig2, bbox_inches='tight')
+    plt.close()
+
+    # Page 3: Tile locations
+    fig3, ax3 = plt.subplots(figsize=(10, 10))
+    tiler = RandomTiler(tile_size=(512, 512), n_tiles=30)
+    tiler.locate_tiles(slide)
+    pdf.savefig(fig3, bbox_inches='tight')
+    plt.close()
+```
+
+## Interactive Visualization (Jupyter)
+
+### IPython Widgets for Exploration
+
+```python
+from ipywidgets import interact, IntSlider
+import matplotlib.pyplot as plt
+from histolab.filters.morphological_filters import BinaryDilation
+
+@interact(disk_size=IntSlider(min=1, max=20, value=5))
+def explore_dilation(disk_size):
+    """Interactive dilation exploration."""
+    filter_pipeline = Compose([
+        RgbToGrayscale(),
+        OtsuThreshold(),
+        BinaryDilation(disk_size=disk_size)
+    ])
+    result = filter_pipeline(slide.thumbnail)
+
+    plt.figure(figsize=(10, 10))
+    plt.imshow(result, cmap='gray')
+    plt.title(f"Binary Dilation (disk_size={disk_size})")
+    plt.axis('off')
+    plt.show()
+```
+
+## Best Practices
+
+1. **Always preview before processing**: Use thumbnails and `locate_tiles()` to validate settings
+2. **Use side-by-side comparisons**: Show before/after for filter effects
+3. **Label clearly**: Include titles, axes labels, and legends
+4. **Export high-resolution**: Use 300 DPI for publication-quality figures
+5. **Save intermediate visualizations**: Document processing steps
+6. **Use colormaps appropriately**: 'gray' for binary masks, 'viridis' for heatmaps
+7. **Create reusable visualization functions**: Standardize reporting across projects