.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "example_gallery/plot_gcnr.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. .. rst-class:: sphx-glr-example-title .. _sphx_glr_example_gallery_plot_gcnr.py: Generalized Contrast-to-Noise Ratio (gCNR) Metric ==================================================== This example demonstrates how to: 1. Load an ultrasound dataset 2. Choose ROI selection mode (interactive or hardcoded) 3. Visualize the B-mode image and select signal/noise ROIs 4. Print ROI summary and statistics 5. Compute and print the gCNR (optionally plot PDFs) This workflow is backend-agnostic and uses the helper functions from ultrasound_metrics. The gCNR metric follows the formulation in [1]_ and uses the PICMUS dataset [2]_. References ---------- .. [1] A. Rodriguez-Molares, O. M. Hoel Rindal, J. D'hooge, S. -E. Måsøy, A. Austeng and H. Torp, "The Generalized Contrast-to-Noise Ratio," 2018 IEEE International Ultrasonics Symposium (IUS), Kobe, Japan, 2018, pp. 1-4, doi: 10.1109/ULTSYM.2018.8580101. .. [2] H. Liebgott, A. Rodriguez-Molares, F. Cervenansky, J. A. Jensen and O. Bernard, "Plane-Wave Imaging Challenge in Medical Ultrasound," 2016 IEEE International Ultrasonics Symposium (IUS), Tours, France, 2016, pp. 1-4, doi: 10.1109/ULTSYM.2016.7728908. Example ------- .. GENERATED FROM PYTHON SOURCE LINES 33-35 1. Import required modules and functions ------------------------------------------------------------------------ .. GENERATED FROM PYTHON SOURCE LINES 35-46 .. code-block:: Python import matplotlib.pyplot as plt import numpy as np from ultrasound_metrics.data import db_zero # (Optional) for interactive ROI selection from ultrasound_metrics.interactive.napari_utils import load_ultrasound_for_napari, select_rois_with_labels from ultrasound_metrics.metrics.gcnr import gcnr from ultrasound_metrics.roi.masks import build_mask .. GENERATED FROM PYTHON SOURCE LINES 47-49 2. Set ROI selection mode ------------------------------------------------------------------------ .. GENERATED FROM PYTHON SOURCE LINES 49-67 .. code-block:: Python def is_headless_environment(): """Detect if running in headless environment (no GUI available).""" import os import sys # Check for ReadTheDocs if os.environ.get("READTHEDOCS") == "True": return True # Check for sphinx-gallery module return "sphinx_gallery" in sys.modules # Interactive if not headless (feel free to change to True / False for local testing) use_interactive = not is_headless_environment() .. GENERATED FROM PYTHON SOURCE LINES 68-70 3. Load the ultrasound dataset ------------------------------------------------------------------------ .. GENERATED FROM PYTHON SOURCE LINES 70-73 .. code-block:: Python image_data, metadata, scan = load_ultrasound_for_napari("picmus_resolution_experiment") .. GENERATED FROM PYTHON SOURCE LINES 74-76 4. Visualize the B-mode image to help select ROI center and radii ------------------------------------------------------------------------ .. GENERATED FROM PYTHON SOURCE LINES 76-102 .. code-block:: Python x_coords = metadata["x_axis"] z_coords = metadata["z_axis"] # Only convert to dB if not already in dB scale if metadata.get("use_db_scale", True): image_db = image_data else: image_db = db_zero(image_data) if not use_interactive: plt.figure(figsize=(7, 7)) plt.imshow( image_db, cmap="gray", aspect="equal", vmin=-60, vmax=0, extent=[x_coords.min(), x_coords.max(), z_coords.max(), z_coords.min()], ) plt.title("B-mode (dB scale)\nNormalized to 0dB") plt.xlabel("Lateral Position (m)") plt.ylabel("Axial Position (m)") plt.colorbar(label="dB") plt.show() .. image-sg:: /example_gallery/images/sphx_glr_plot_gcnr_001.png :alt: B-mode (dB scale) Normalized to 0dB :srcset: /example_gallery/images/sphx_glr_plot_gcnr_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 103-105 5. Select ROIs (either interactively or with hardcoded masks) ------------------------------------------------------------------------ .. GENERATED FROM PYTHON SOURCE LINES 105-150 .. code-block:: Python if use_interactive: roi_result = select_rois_with_labels(image=image_data) mask_signal = roi_result["mask_signal"] mask_noise = roi_result["mask_noise"] else: # Hardcoded ROI selection and visualization center = (-0.0105, 0.028) # (x, z) in meters signal_radius = 0.004 # 4 mm noise_radius = 0.0075 # 7.5 mm mask_signal = build_mask( position=center, dimension=signal_radius, x_axis=metadata["x_axis"], z_axis=metadata["z_axis"], shape="circle" ) mask_noise_outer = build_mask( position=center, dimension=noise_radius, x_axis=metadata["x_axis"], z_axis=metadata["z_axis"], shape="circle" ) mask_noise = np.logical_and(mask_noise_outer, ~mask_signal) # --- Visualization of B-mode, signal, and noise masks --- fig, axs = plt.subplots(1, 3, figsize=(15, 5)) bmode_args = { "extent": [x_coords.min(), x_coords.max(), z_coords.max(), z_coords.min()], "cmap": "gray", "vmin": -60, "vmax": 0, } im0 = axs[0].imshow(image_db, **bmode_args) axs[0].set_title("B-mode (dB)") axs[0].set_xlabel("Lateral (m)") axs[0].set_ylabel("Axial (m)") fig.colorbar(im0, ax=axs[0], orientation="vertical", label="dB") masked_signal = np.ma.masked_where(~mask_signal, image_db) im1 = axs[1].imshow(masked_signal, **bmode_args) axs[1].set_title("Signal ROI") axs[1].set_xlabel("Lateral (m)") axs[1].set_ylabel("Axial (m)") fig.colorbar(im1, ax=axs[1], orientation="vertical", label="dB") masked_noise = np.ma.masked_where(~mask_noise, image_db) im2 = axs[2].imshow(masked_noise, **bmode_args) axs[2].set_title("Noise ROI") axs[2].set_xlabel("Lateral (m)") axs[2].set_ylabel("Axial (m)") fig.colorbar(im2, ax=axs[2], orientation="vertical", label="dB") plt.tight_layout() plt.show() .. image-sg:: /example_gallery/images/sphx_glr_plot_gcnr_002.png :alt: B-mode (dB), Signal ROI, Noise ROI :srcset: /example_gallery/images/sphx_glr_plot_gcnr_002.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 151-153 6. Print ROI summary ------------------------------------------------------------------------ .. GENERATED FROM PYTHON SOURCE LINES 153-160 .. code-block:: Python signal_pixels = mask_signal.sum() noise_pixels = mask_noise.sum() print("\n=== ROI Selection Summary ===") print(f"Signal region pixel count: {signal_pixels}") print(f"Noise region pixel count: {noise_pixels}") .. rst-class:: sphx-glr-script-out .. code-block:: none === ROI Selection Summary === Signal region pixel count: 6895 Noise region pixel count: 17361 .. GENERATED FROM PYTHON SOURCE LINES 161-163 7. Print basic statistics ------------------------------------------------------------------------ .. GENERATED FROM PYTHON SOURCE LINES 163-170 .. code-block:: Python values_signal = image_data[mask_signal] values_noise = image_data[mask_noise] print("\n=== Region Statistics ===") print(f"Signal mean: {values_signal.mean():.3f} dB") print(f"Noise mean: {values_noise.mean():.3f} dB") .. rst-class:: sphx-glr-script-out .. code-block:: none === Region Statistics === Signal mean: -23.187 dB Noise mean: -35.079 dB .. GENERATED FROM PYTHON SOURCE LINES 171-173 8. Compute and print gCNR (optionally plot PDFs) ------------------------------------------------------------------------ .. GENERATED FROM PYTHON SOURCE LINES 173-181 .. code-block:: Python fig, ax = plt.subplots(figsize=(6, 4)) gcnr_value = gcnr(values_inside=values_signal, values_outside=values_noise, ax=ax) plt.tight_layout() plt.show() print("\n=== gCNR Results ===") print(f"gCNR: {gcnr_value:.3f}") .. image-sg:: /example_gallery/images/sphx_glr_plot_gcnr_003.png :alt: gCNR = 0.6949, Pmax = 0.8474 :srcset: /example_gallery/images/sphx_glr_plot_gcnr_003.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none === gCNR Results === gCNR: 0.695 .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.572 seconds) .. _sphx_glr_download_example_gallery_plot_gcnr.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_gcnr.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_gcnr.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_gcnr.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_