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"# Import necessary libraries\n",
"import os\n",
"import numpy as np\n",
"from PIL import Image\n",
"from skimage.color import rgb2gray\n",
"from skimage.filters import sobel\n",
"import plotly.graph_objects as go\n",
"from ipywidgets import interact, IntRangeSlider\n",
"import logging\n",
"\n",
"# Configure logging\n",
"logging.basicConfig(level=logging.INFO, format=\"%(asctime)s - %(levelname)s - %(message)s\")\n",
"logger = logging.getLogger(__name__)\n"
]
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{
"cell_type": "code",
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"source": [
"def load_data_with_masks(folder_path):\n",
" \"\"\"\n",
" Load MRI slices and corresponding masks from the specified folder.\n",
"\n",
" Parameters:\n",
" folder_path (str): Path to folder containing slices and masks.\n",
"\n",
" Returns:\n",
" list: List of tuples (slice_array, mask_array).\n",
" \"\"\"\n",
" logger.info(\"Loading data from folder...\")\n",
" data = []\n",
" files = sorted(os.listdir(folder_path))\n",
" for file in files:\n",
" if file.endswith(\".tif\") and not file.endswith(\"_mask.tif\"):\n",
" slice_path = os.path.join(folder_path, file)\n",
" mask_path = os.path.join(folder_path, file.replace(\".tif\", \"_mask.tif\"))\n",
" if os.path.exists(mask_path):\n",
" slice_img = Image.open(slice_path)\n",
" mask_img = Image.open(mask_path)\n",
" data.append((np.array(slice_img), np.array(mask_img)))\n",
" else:\n",
" logger.warning(f\"Mask file missing for slice: {file}\")\n",
" logger.info(f\"Loaded {len(data)} slice-mask pairs.\")\n",
" return data\n"
]
},
{
"cell_type": "code",
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"source": [
"def preprocess_data(data, target_shape=(256, 256)):\n",
" \"\"\"\n",
" Preprocess slice-mask pairs: normalize and pad to a uniform size.\n",
"\n",
" Parameters:\n",
" data (list): List of tuples (slice, mask).\n",
" target_shape (tuple): Target shape for all slices and masks.\n",
"\n",
" Returns:\n",
" list: List of preprocessed tuples (slice, mask).\n",
" \"\"\"\n",
" processed = []\n",
" for i, (slice_img, mask_img) in enumerate(data):\n",
" logger.info(f\"Preprocessing slice {i + 1}/{len(data)}\")\n",
" if len(slice_img.shape) == 3: # Handle RGB images\n",
" slice_img = slice_img[:, :, :3] # Ensure only 3 channels\n",
" \n",
" slice_img = np.pad(slice_img, ((0, target_shape[0] - slice_img.shape[0]),\n",
" (0, target_shape[1] - slice_img.shape[1]),\n",
" (0, 0)), mode=\"constant\", constant_values=0)\n",
" processed.append((slice_img, mask_img))\n",
" logger.info(f\"Preprocessed {len(processed)} slices.\")\n",
" return processed\n"
]
},
{
"cell_type": "code",
"execution_count": null,
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"execution": {
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"source": [
"# Path to folder containing MRI slices and masks\n",
"folder_path = \"/kaggle/input/lgg-mri-segmentation/kaggle_3m/TCGA_CS_4941_19960909\"\n",
"\n",
"# Load and preprocess the data\n",
"slice_mask_pairs = load_data_with_masks(folder_path)\n",
"processed_slice_mask_pairs = preprocess_data(slice_mask_pairs)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
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"source": [
"def filter_with_sobel(slice_img):\n",
" \"\"\"\n",
" Apply Sobel filtering to remove white space from each RGB layer.\n",
"\n",
" Parameters:\n",
" slice_img (np.ndarray): RGB slice image.\n",
"\n",
" Returns:\n",
" np.ndarray: Filtered RGB slice.\n",
" \"\"\"\n",
" grayscale = rgb2gray(slice_img)\n",
" sobel_mask = sobel(grayscale) > 0.1 # Binary mask (thresholded Sobel edges)\n",
" \n",
" # Apply the Sobel mask to each RGB layer\n",
" filtered_img = np.zeros_like(slice_img)\n",
" for channel in range(3): # Apply to R, G, and B layers\n",
" filtered_img[:, :, channel] = slice_img[:, :, channel] * sobel_mask\n",
"\n",
" return filtered_img\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "382a8a64",
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},
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"source": [
"def apply_sobel_to_slices(data):\n",
" \"\"\"\n",
" Apply Sobel filtering to all RGB slices in the dataset.\n",
"\n",
" Parameters:\n",
" data (list): Preprocessed slice-mask pairs.\n",
"\n",
" Returns:\n",
" list: Filtered RGB slices.\n",
" \"\"\"\n",
" filtered_data = []\n",
" for i, (slice_img, mask_img) in enumerate(data):\n",
" logger.info(f\"Applying Sobel filtering to slice {i + 1}/{len(data)}\")\n",
" filtered_img = filter_with_sobel(slice_img)\n",
" filtered_data.append((filtered_img, mask_img))\n",
" return filtered_data\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3d16e7c9",
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},
"tags": []
},
"outputs": [],
"source": [
"def create_cube4d(data):\n",
" \"\"\"\n",
" Create a Cube4D structure by stacking filtered RGB slices.\n",
"\n",
" Parameters:\n",
" data (list): Filtered slice-mask pairs.\n",
"\n",
" Returns:\n",
" np.ndarray: Cube4D matrix (z, height, width, channels).\n",
" \"\"\"\n",
" z_slices = len(data)\n",
" height, width, channels = data[0][0].shape\n",
" cube4d = np.zeros((z_slices, height, width, channels), dtype=np.uint8)\n",
"\n",
" for z, (slice_img, _) in enumerate(data):\n",
" cube4d[z] = slice_img\n",
"\n",
" return cube4d\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "06671995",
"metadata": {
"execution": {
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"outputs": [],
"source": [
"from ipywidgets import interact, IntRangeSlider\n",
"\n",
"def visualize_with_layer_range_filter_dynamic(rgb_slices, purple_threshold=(130, 80, 140)):\n",
" \"\"\"\n",
" Visualize the Cube4D structure dynamically with a range slider to control visible layers.\n",
"\n",
" Parameters:\n",
" rgb_slices (list): List of processed RGB slices.\n",
" purple_threshold (tuple): RGB threshold to identify and exclude \"purple\" areas (R, G, B values).\n",
" \"\"\"\n",
" def plot_layers(slice_range):\n",
" \"\"\"\n",
" Sub-function to dynamically plot layers within the specified range.\n",
"\n",
" Parameters:\n",
" slice_range (tuple): Start and end indices for the slice range to display.\n",
" \"\"\"\n",
" fig = go.Figure()\n",
" r_thresh, g_thresh, b_thresh = purple_threshold\n",
"\n",
" for z in range(slice_range[0], slice_range[1] + 1):\n",
" rgb_image = rgb_slices[z]\n",
"\n",
" # Create a boolean mask for \"purple\" regions\n",
" purple_mask = (\n",
" (rgb_image[..., 0] >= r_thresh - 10) & (rgb_image[..., 0] <= r_thresh + 10) &\n",
" (rgb_image[..., 1] >= g_thresh - 10) & (rgb_image[..., 1] <= g_thresh + 10) &\n",
" (rgb_image[..., 2] >= b_thresh - 10) & (rgb_image[..., 2] <= b_thresh + 10)\n",
" )\n",
"\n",
" # Replace purple areas with NaN\n",
" filtered_image = np.mean(rgb_image, axis=2).astype(float)\n",
" filtered_image[purple_mask] = np.nan\n",
"\n",
" # Add slice to the figure\n",
" height, width = filtered_image.shape\n",
" fig.add_trace(go.Surface(\n",
" z=np.full((height, width), z),\n",
" x=np.arange(width),\n",
" y=np.arange(height),\n",
" surfacecolor=filtered_image,\n",
" colorscale=\"Viridis\",\n",
" opacity=0.8,\n",
" showscale=False\n",
" ))\n",
"\n",
" # Customize layout\n",
" fig.update_layout(\n",
" title=f\"3D RGB Visualization (Slices {slice_range[0]}-{slice_range[1]})\",\n",
" scene=dict(\n",
" zaxis_title=\"Slices\",\n",
" xaxis_title=\"Width\",\n",
" yaxis_title=\"Height\"\n",
" )\n",
" )\n",
" fig.show()\n",
"\n",
" # Interactive range slider for layer range\n",
" interact(plot_layers, slice_range=IntRangeSlider(\n",
" value=(0, len(rgb_slices) - 1), # Default range\n",
" min=0,\n",
" max=len(rgb_slices) - 1,\n",
" step=1,\n",
" continuous_update=True, # Enable live updates\n",
" description=\"Layer Range\"\n",
" ))\n"
]
},
{
"cell_type": "code",
"execution_count": null,
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},
"outputs": [],
"source": [
"def generate_composite_masks(rgb_slices, threshold=10):\n",
" \"\"\"\n",
" Generate composite masks for all slices to represent areas of content.\n",
"\n",
" Parameters:\n",
" rgb_slices (list): List of RGB slices (H, W, 3).\n",
" threshold (int): Minimum intensity to consider as content.\n",
"\n",
" Returns:\n",
" list: Composite masks for each slice (H, W), where 0 = content and 1 = empty.\n",
" \"\"\"\n",
" composite_masks = []\n",
" for i, rgb_image in enumerate(rgb_slices):\n",
" logger.info(f\"Generating composite mask for slice {i + 1}/{len(rgb_slices)}\")\n",
"\n",
" # Convert RGB slice to grayscale intensity\n",
" intensity = np.mean(rgb_image, axis=2)\n",
"\n",
" # Create a boolean mask (1 = no content, 0 = content)\n",
" mask = (intensity < threshold).astype(np.uint8)\n",
" composite_masks.append(mask)\n",
"\n",
" return composite_masks\n"
]
},
{
"cell_type": "code",
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"source": [
"def overlay_composite_masks_with_rgb(rgb_slices, composite_masks, opacity=0.8):\n",
" \"\"\"\n",
" Overlay composite masks with RGB intensity for visualization.\n",
"\n",
" Parameters:\n",
" rgb_slices (list): List of RGB slices (H, W, 3).\n",
" composite_masks (list): List of composite masks (H, W).\n",
" opacity (float): Default opacity for the brain material.\n",
"\n",
" Returns:\n",
" list: Processed slices for visualization.\n",
" \"\"\"\n",
" processed_slices = []\n",
" for i, (rgb_image, mask) in enumerate(zip(rgb_slices, composite_masks)):\n",
" logger.info(f\"Overlaying mask on slice {i + 1}/{len(rgb_slices)}\")\n",
"\n",
" # Normalize RGB intensity to a range of [0, 1]\n",
" intensity = np.mean(rgb_image, axis=2) / 255.0\n",
"\n",
" # Apply mask: Keep intensity only where mask allows (content regions)\n",
" filtered_intensity = intensity * (1 - mask) # Invert mask (0 = content)\n",
"\n",
" # Scale intensity with opacity\n",
" filtered_intensity *= opacity\n",
" processed_slices.append(filtered_intensity)\n",
"\n",
" return processed_slices\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "12c31e0c",
"metadata": {
"execution": {
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"source": [
"from ipywidgets import interact, FloatSlider\n",
"\n",
"def visualize_composite_masks(processed_slices):\n",
" \"\"\"\n",
" Visualize the composite mask overlay in 3D.\n",
"\n",
" Parameters:\n",
" processed_slices (list): List of processed slices (H, W).\n",
" \"\"\"\n",
" def plot_layers(opacity):\n",
" fig = go.Figure()\n",
"\n",
" for z, slice_image in enumerate(processed_slices):\n",
" # Scale intensity by current opacity\n",
" visual_intensity = slice_image * opacity\n",
"\n",
" # Add slice to the figure\n",
" height, width = visual_intensity.shape\n",
" fig.add_trace(go.Surface(\n",
" z=np.full((height, width), z),\n",
" x=np.arange(width),\n",
" y=np.arange(height),\n",
" surfacecolor=visual_intensity,\n",
" colorscale=\"Greys\", # Black and white visualization\n",
" opacity=opacity,\n",
" showscale=False\n",
" ))\n",
"\n",
" # Customize layout\n",
" fig.update_layout(\n",
" title=f\"3D Composite Mask Visualization (Opacity: {opacity:.2f})\",\n",
" scene=dict(\n",
" zaxis_title=\"Slices\",\n",
" xaxis_title=\"Width\",\n",
" yaxis_title=\"Height\"\n",
" )\n",
" )\n",
" fig.show()\n",
"\n",
" # Interactive slider for opacity adjustment\n",
" interact(plot_layers, opacity=FloatSlider(\n",
" value=0.8, # Default opacity\n",
" min=0.1,\n",
" max=1.0,\n",
" step=0.1,\n",
" description=\"Opacity\"\n",
" ))\n"
]
},
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"source": [
"def filter_purple(slice_img, purple_rgb=(131, 84, 141), threshold=50):\n",
" \"\"\"\n",
" Replace purple regions with NaN for transparency.\n",
"\n",
" Parameters:\n",
" slice_img (np.ndarray): RGB slice image.\n",
" purple_rgb (tuple): RGB values of the purple color to filter out.\n",
" threshold (int): Allowable difference from the purple color.\n",
"\n",
" Returns:\n",
" np.ndarray: RGB slice with purple regions replaced by NaN.\n",
" \"\"\"\n",
" # Calculate the Euclidean distance from the purple color\n",
" distance = np.sqrt(\n",
" (slice_img[..., 0] - purple_rgb[0]) ** 2 +\n",
" (slice_img[..., 1] - purple_rgb[1]) ** 2 +\n",
" (slice_img[..., 2] - purple_rgb[2]) ** 2\n",
" )\n",
" \n",
" # Create a mask for pixels close to purple\n",
" purple_mask = distance < threshold\n",
" \n",
" # Replace purple pixels with NaN\n",
" filtered_slice = slice_img.astype(float)\n",
" for channel in range(3):\n",
" filtered_slice[..., channel][purple_mask] = np.nan # Set to NaN for transparency\n",
"\n",
" return filtered_slice\n",
"\n",
"def apply_purple_filter_to_slices(data, purple_rgb=(131, 84, 141), threshold=50):\n",
" \"\"\"\n",
" Apply purple filtering to all slices in the dataset.\n",
"\n",
" Parameters:\n",
" data (list): List of RGB slices.\n",
" purple_rgb (tuple): RGB values of the purple color to filter out.\n",
" threshold (int): Allowable difference from the purple color.\n",
"\n",
" Returns:\n",
" list: List of slices with purple regions made transparent.\n",
" \"\"\"\n",
" filtered_slices = []\n",
" for i, (slice_img, _) in enumerate(data):\n",
" logger.info(f\"Filtering purple for slice {i + 1}/{len(data)}\")\n",
" filtered_img = filter_purple(slice_img, purple_rgb, threshold)\n",
" filtered_slices.append(filtered_img)\n",
" return filtered_slices\n",
"\n",
"# Apply the purple filter to slices\n",
"filtered_rgb_slices = apply_purple_filter_to_slices(processed_slice_mask_pairs)\n",
"\n",
"def visualize_no_purple_cube(rgb_slices):\n",
" \"\"\"\n",
" Visualize the RGB slices as a 3D cube with purple regions made transparent.\n",
"\n",
" Parameters:\n",
" rgb_slices (list): List of RGB slices with purple regions filtered out.\n",
" \"\"\"\n",
" z_slices = len(rgb_slices)\n",
" height, width, _ = rgb_slices[0].shape\n",
"\n",
" fig = go.Figure()\n",
"\n",
" # Render each slice with purple removed\n",
" for z, rgb_image in enumerate(rgb_slices):\n",
" # Normalize to [0, 1] for visualization\n",
" normalized_rgb = rgb_image / 255.0\n",
"\n",
" # Use the mean intensity as the surface color\n",
" surfacecolor = np.nanmean(normalized_rgb, axis=2) # Use nanmean to handle NaNs\n",
"\n",
" fig.add_trace(go.Surface(\n",
" z=np.full((height, width), z), # Set slice depth\n",
" x=np.arange(width),\n",
" y=np.arange(height),\n",
" surfacecolor=surfacecolor, # Use filtered surface color\n",
" colorscale=\"Viridis\",\n",
" opacity=0.8,\n",
" showscale=False\n",
" ))\n",
"\n",
" fig.update_layout(\n",
" title=\"3D RGB Visualization Without Purple\",\n",
" scene=dict(\n",
" zaxis_title=\"Slices\",\n",
" xaxis_title=\"Width\",\n",
" yaxis_title=\"Height\"\n",
" )\n",
" )\n",
"\n",
" fig.show()\n",
"\n",
"# Visualize the cube with purple regions removed\n",
"visualize_no_purple_cube(filtered_rgb_slices)\n"
]
}
],
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