.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "example_gallery/plot_tenengrad.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_tenengrad.py: Tenengrad Sharpness Metric ==================================================== This example uses a PICMUS dataset to demonstrate how to: 1. Compute and visualize the Tenengrad sharpness metric [1]_ 2. Compare sharpness between datasets with different characteristics 3. Show how the metric responds to different ROI selections [2]_ uses the Tenengrad sharpness metric to compare the lateral focusing quality across different speed-of-sound profiles. References ---------- .. [1] Groen, F. C., Young, I. T., & Ligthart, G. (1985). A comparison of different focus functions for use in autrasound algorithms. Cytometry, 6(2), 81-91. .. [2] Vrålstad, A.E. et al. (2024). Coherence Based Sound Speed Aberration Correction — with clinical validation in fetal ultrasound. arXiv:2411.16551 Example ------- .. GENERATED FROM PYTHON SOURCE LINES 28-30 1. Import required modules and functions ------------------------------------------------------------------------ .. GENERATED FROM PYTHON SOURCE LINES 30-39 .. code-block:: Python import matplotlib.pyplot as plt import numpy as np from ultrasound_metrics.data.uff import load_uff_dataset from ultrasound_metrics.data.visualize_bmode import db_zero from ultrasound_metrics.metrics.sharpness import tenengrad from ultrasound_metrics.roi.masks import build_mask .. GENERATED FROM PYTHON SOURCE LINES 40-42 2. Load PICMUS datasets ------------------------------------------------------------------------ .. GENERATED FROM PYTHON SOURCE LINES 42-52 .. code-block:: Python image_resolution, scan_resolution = load_uff_dataset("picmus_resolution_experiment") image_resolution = image_resolution.T image_contrast, scan_contrast = load_uff_dataset("picmus_contrast_experiment") image_contrast = image_contrast.T print(f"Resolution/distortion dataset image shape: {image_resolution.shape}") print(f"Contrast/speckle dataset image shape: {image_contrast.shape}") .. rst-class:: sphx-glr-script-out .. code-block:: none Resolution/distortion dataset image shape: (609, 387) Contrast/speckle dataset image shape: (609, 387) .. GENERATED FROM PYTHON SOURCE LINES 53-56 3. Calculate edge-enhanced image ------------------------------------------------------------------------ This is an intermediate step for Tenengrad sharpness. .. GENERATED FROM PYTHON SOURCE LINES 56-63 .. code-block:: Python sharpness_img_resolution = tenengrad(image_resolution, reduce_sum=False) print("sharpness image shape:", sharpness_img_resolution.shape, "(resolution/distortion dataset)") sharpness_img_contrast = tenengrad(image_contrast, reduce_sum=False) print("sharpness image shape:", sharpness_img_contrast.shape, "(contrast/speckle dataset)") .. rst-class:: sphx-glr-script-out .. code-block:: none sharpness image shape: (609, 387) (resolution/distortion dataset) sharpness image shape: (609, 387) (contrast/speckle dataset) .. GENERATED FROM PYTHON SOURCE LINES 64-66 4. Visualize the original images and their sharpness maps ------------------------------------------------------------------------ .. GENERATED FROM PYTHON SOURCE LINES 66-134 .. code-block:: Python fig, axes = plt.subplots(2, 3, figsize=(15, 10), gridspec_kw={"width_ratios": [1, 1, 1]}) # Share x and y axes for the histogram plots (right column) axes[0, 2].sharex(axes[1, 2]) axes[0, 2].sharey(axes[1, 2]) # Get coordinate arrays for proper axis labeling x_coords_res = scan_resolution.x_axis z_coords_res = scan_resolution.z_axis x_coords_con = scan_contrast.x_axis z_coords_con = scan_contrast.z_axis # Define common visualization parameters vmin = -60 # dB range for B-mode images vmax = 0 sharpness_vmin = 0 sharpness_vmax = 2 # Row 1: Resolution/distortion dataset extent_res = [x_coords_res.min(), x_coords_res.max(), z_coords_res.max(), z_coords_res.min()] im1 = axes[0, 0].imshow(db_zero(image_resolution), cmap="gray", vmin=vmin, vmax=vmax, extent=extent_res) axes[0, 0].set_title("B-mode (dB)\n(Resolution/distortion dataset)") axes[0, 0].set_xlabel("Lateral Position (m)") axes[0, 0].set_ylabel("Axial Position (m)") im2 = axes[0, 1].imshow( sharpness_img_resolution, cmap="hot", extent=extent_res, vmin=sharpness_vmin, vmax=sharpness_vmax ) axes[0, 1].set_title("Tenengrad Sharpness Image\n(Resolution/distortion dataset)") axes[0, 1].set_xlabel("Lateral Position (m)") axes[0, 1].set_ylabel("Axial Position (m)") # Histogram of sharpness values for resolution/distortion dataset axes[0, 2].hist(sharpness_img_resolution.flatten(), bins=50, alpha=0.7, color="red") axes[0, 2].set_title("Sharpness Value Distribution\n(Resolution/distortion dataset)") axes[0, 2].set_xlabel("Sharpness Value") axes[0, 2].set_ylabel("Frequency") axes[0, 2].set_yscale("log") # Row 2: Contrast/speckle dataset extent_con = [x_coords_con.min(), x_coords_con.max(), z_coords_con.max(), z_coords_con.min()] im3 = axes[1, 0].imshow(db_zero(image_contrast), cmap="gray", vmin=vmin, vmax=vmax, extent=extent_con) axes[1, 0].set_title("B-mode (dB)\n(Contrast/speckle dataset)") axes[1, 0].set_xlabel("Lateral Position (m)") axes[1, 0].set_ylabel("Axial Position (m)") im4 = axes[1, 1].imshow(sharpness_img_contrast, cmap="hot", extent=extent_con, vmin=sharpness_vmin, vmax=sharpness_vmax) axes[1, 1].set_title("Tenengrad Sharpness Image\n(Contrast/speckle dataset)") axes[1, 1].set_xlabel("Lateral Position (m)") axes[1, 1].set_ylabel("Axial Position (m)") # Histogram of sharpness values for contrast experiment axes[1, 2].hist(sharpness_img_contrast.flatten(), bins=50, alpha=0.7, color="blue") axes[1, 2].set_title("Sharpness Value Distribution\n(Contrast/speckle dataset)") axes[1, 2].set_xlabel("Sharpness Value") axes[1, 2].set_ylabel("Frequency") axes[1, 2].set_yscale("log") # Add colorbars plt.colorbar(im1, ax=axes[0, 0], label="dB") plt.colorbar(im3, ax=axes[1, 0], label="dB") plt.colorbar(im2, ax=axes[0, 1], label="Lateral Sharpness") plt.colorbar(im4, ax=axes[1, 1], label="Lateral Sharpness") plt.tight_layout() plt.show() .. image-sg:: /example_gallery/images/sphx_glr_plot_tenengrad_001.png :alt: B-mode (dB) (Resolution/distortion dataset), Tenengrad Sharpness Image (Resolution/distortion dataset), Sharpness Value Distribution (Resolution/distortion dataset), B-mode (dB) (Contrast/speckle dataset), Tenengrad Sharpness Image (Contrast/speckle dataset), Sharpness Value Distribution (Contrast/speckle dataset) :srcset: /example_gallery/images/sphx_glr_plot_tenengrad_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 135-137 5. Calculate overall sharpness metrics for both datasets ------------------------------------------------------------------------ .. GENERATED FROM PYTHON SOURCE LINES 137-167 .. code-block:: Python print("\n=== Overall Tenengrad Sharpness Comparison ===") # Calculate total sharpness (sum) and normalized sharpness (mean) sharpness_resolution = tenengrad(image_resolution, normalize=False) sharpness_normalized_resolution = tenengrad(image_resolution, normalize=True) sharpness_contrast = tenengrad(image_contrast, normalize=False) sharpness_normalized_contrast = tenengrad(image_contrast, normalize=True) print("Resolution experiment:") print(f" Total sharpness: {float(sharpness_resolution):.1f}") print(f" Normalized sharpness: {float(sharpness_normalized_resolution):.4f}") print("\nContrast experiment:") print(f" Total sharpness: {float(sharpness_contrast):.1f}") print(f" Normalized sharpness: {float(sharpness_normalized_contrast):.4f}") # Calculate ratio ratio = float(sharpness_normalized_resolution) / float(sharpness_normalized_contrast) print(f"\nSharpness ratio (Resolution/Contrast): {ratio:.2f}") if ratio > 1.2: print("✅ Resolution/distortion dataset shows higher sharpness as expected!") print(" Point targets create sharper edges than diffuse lesions.") elif ratio > 0.8: print("️Similar overall sharpness between datasets.") else: print("⚠️ Contrast/speckle dataset shows higher sharpness.") .. rst-class:: sphx-glr-script-out .. code-block:: none === Overall Tenengrad Sharpness Comparison === Resolution experiment: Total sharpness: 35788.9 Normalized sharpness: 0.1519 Contrast experiment: Total sharpness: 35172.2 Normalized sharpness: 0.1492 Sharpness ratio (Resolution/Contrast): 1.02 ️Similar overall sharpness between datasets. .. GENERATED FROM PYTHON SOURCE LINES 168-173 6. Compare sharpness across regions ------------------------------------------------------------------------ We should expect that the point target region is sharper than the background speckle region. Here, we visualize the sharpness in different regions and validate this expectation. .. GENERATED FROM PYTHON SOURCE LINES 173-250 .. code-block:: Python # For the resolution experiment, create ROIs around different areas center_target = (0.000, 0.0275) # Center of a point target (x, z in meters) center_background = (0.015, 0.0275) # Background speckle region # Create circular ROIs target_radius = 0.003 # 3mm radius background_radius = 0.005 # 5mm radius # Build masks for the resolution experiment mask_target = build_mask( position=center_target, dimension=target_radius, x_axis=scan_resolution.x_axis, z_axis=scan_resolution.z_axis, shape="circle", ) mask_background = build_mask( position=center_background, dimension=background_radius, x_axis=scan_resolution.x_axis, z_axis=scan_resolution.z_axis, shape="circle", ) fig, axes = plt.subplots(1, 3, figsize=(15, 5)) # Show original image with ROI overlays extent = [x_coords_res.min(), x_coords_res.max(), z_coords_res.max(), z_coords_res.min()] # Original image im1 = axes[0].imshow(db_zero(image_resolution), cmap="gray", vmin=vmin, vmax=vmax, extent=extent) axes[0].contour( np.linspace(extent[0], extent[1], mask_target.shape[1]), np.linspace(extent[3], extent[2], mask_target.shape[0]), mask_target, colors="red", linewidths=2, ) axes[0].contour( np.linspace(extent[0], extent[1], mask_background.shape[1]), np.linspace(extent[3], extent[2], mask_background.shape[0]), mask_background, colors="blue", linewidths=2, ) axes[0].set_title("B-mode with ROIs\n(Red: Target, Blue: Background)") axes[0].set_xlabel("Lateral Position (m)") axes[0].set_ylabel("Axial Position (m)") # Calculate sharpness in different ROIs sharpness_target = tenengrad(image_resolution, roi=mask_target, normalize=True) sharpness_background = tenengrad(image_resolution, roi=mask_background, normalize=True) # Target ROI masked_target = np.ma.masked_where(~mask_target, sharpness_img_resolution) im2 = axes[1].imshow(masked_target, cmap="hot", extent=extent, vmin=sharpness_vmin, vmax=sharpness_vmax) axes[1].set_title(f"Target ROI Sharpness\nMean: {float(sharpness_target):.4f}") axes[1].set_xlabel("Lateral Position (m)") axes[1].set_ylabel("Axial Position (m)") # Background ROI masked_background = np.ma.masked_where(~mask_background, sharpness_img_resolution) im3 = axes[2].imshow(masked_background, cmap="hot", extent=extent, vmin=sharpness_vmin, vmax=sharpness_vmax) axes[2].set_title(f"Background ROI Sharpness\nMean: {float(sharpness_background):.4f}") axes[2].set_xlabel("Lateral Position (m)") axes[2].set_ylabel("Axial Position (m)") # Add colorbars plt.colorbar(im1, ax=axes[0], label="dB") plt.colorbar(im2, ax=axes[1], label="Lateral Sharpness") plt.colorbar(im3, ax=axes[2], label="Lateral Sharpness") plt.tight_layout() plt.show() .. image-sg:: /example_gallery/images/sphx_glr_plot_tenengrad_002.png :alt: B-mode with ROIs (Red: Target, Blue: Background), Target ROI Sharpness Mean: 0.2650, Background ROI Sharpness Mean: 0.1429 :srcset: /example_gallery/images/sphx_glr_plot_tenengrad_002.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 251-256 7. Compare sharpness across regions ------------------------------------------------------------------------ We should expect that the point target region is sharper than the background speckle region. .. GENERATED FROM PYTHON SOURCE LINES 256-268 .. code-block:: Python print("\n=== ROI-based Sharpness Analysis ===") print(f"Point target region sharpness: {float(sharpness_target):.4f}") print(f"Background speckle sharpness: {float(sharpness_background):.4f}") target_bg_ratio = float(sharpness_target) / float(sharpness_background) print(f"Target/Background ratio: {target_bg_ratio:.2f}") if target_bg_ratio > 1.5: print("✅ Point target region is significantly sharper than background!") print(" This demonstrates the metric's sensitivity to small structures.") .. rst-class:: sphx-glr-script-out .. code-block:: none === ROI-based Sharpness Analysis === Point target region sharpness: 0.2650 Background speckle sharpness: 0.1429 Target/Background ratio: 1.85 ✅ Point target region is significantly sharper than background! This demonstrates the metric's sensitivity to small structures. .. GENERATED FROM PYTHON SOURCE LINES 269-279 8. Summary ------------------------------------------------------------------------ We have shown that the Tenengrad sharpness metric can be used to compare the sharpness of different regions in an image. This metric can be valuable for [2]_: - Assessing ultrasound image focusing quality - Comparing different imaging parameters or techniques - Automated quality control in ultrasound systems .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 1.464 seconds) .. _sphx_glr_download_example_gallery_plot_tenengrad.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_tenengrad.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_tenengrad.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_tenengrad.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_