
- Scikit Image – Introduction
- Scikit Image - Image Processing
- Scikit Image - Numpy Images
- Scikit Image - Image datatypes
- Scikit Image - Using Plugins
- Scikit Image - Image Handlings
- Scikit Image - Reading Images
- Scikit Image - Writing Images
- Scikit Image - Displaying Images
- Scikit Image - Image Collections
- Scikit Image - Image Stack
- Scikit Image - Multi Image
- Scikit Image - Data Visualization
- Scikit Image - Using Matplotlib
- Scikit Image - Using Ploty
- Scikit Image - Using Mayavi
- Scikit Image - Using Napari
- Scikit Image - Color Manipulation
- Scikit Image - Alpha Channel
- Scikit Image - Conversion b/w Color & Gray Values
- Scikit Image - Conversion b/w RGB & HSV
- Scikit Image - Conversion to CIE-LAB Color Space
- Scikit Image - Conversion from CIE-LAB Color Space
- Scikit Image - Conversion to luv Color Space
- Scikit Image - Conversion from luv Color Space
- Scikit Image - Image Inversion
- Scikit Image - Painting Images with Labels
- Scikit Image - Contrast & Exposure
- Scikit Image - Contrast
- Scikit Image - Contrast enhancement
- Scikit Image - Exposure
- Scikit Image - Histogram Matching
- Scikit Image - Histogram Equalization
- Scikit Image - Local Histogram Equalization
- Scikit Image - Tinting gray-scale images
- Scikit Image - Image Transformation
- Scikit Image - Scaling an image
- Scikit Image - Rotating an Image
- Scikit Image - Warping an Image
- Scikit Image - Affine Transform
- Scikit Image - Piecewise Affine Transform
- Scikit Image - ProjectiveTransform
- Scikit Image - EuclideanTransform
- Scikit Image - Radon Transform
- Scikit Image - Line Hough Transform
- Scikit Image - Probabilistic Hough Transform
- Scikit Image - Circular Hough Transforms
- Scikit Image - Elliptical Hough Transforms
- Scikit Image - Polynomial Transform
- Scikit Image - Image Pyramids
- Scikit Image - Pyramid Gaussian Transform
- Scikit Image - Pyramid Laplacian Transform
- Scikit Image - Swirl Transform
- Scikit Image - Morphological Operations
- Scikit Image - Erosion
- Scikit Image - Dilation
- Scikit Image - Black & White Tophat Morphologies
- Scikit Image - Convex Hull
- Scikit Image - Generating footprints
- Scikit Image - Isotopic Dilation & Erosion
- Scikit Image - Isotopic Closing & Opening of an Image
- Scikit Image - Skelitonizing an Image
- Scikit Image - Morphological Thinning
- Scikit Image - Masking an image
- Scikit Image - Area Closing & Opening of an Image
- Scikit Image - Diameter Closing & Opening of an Image
- Scikit Image - Morphological reconstruction of an Image
- Scikit Image - Finding local Maxima
- Scikit Image - Finding local Minima
- Scikit Image - Removing Small Holes from an Image
- Scikit Image - Removing Small Objects from an Image
- Scikit Image - Filters
- Scikit Image - Image Filters
- Scikit Image - Median Filter
- Scikit Image - Mean Filters
- Scikit Image - Morphological gray-level Filters
- Scikit Image - Gabor Filter
- Scikit Image - Gaussian Filter
- Scikit Image - Butterworth Filter
- Scikit Image - Frangi Filter
- Scikit Image - Hessian Filter
- Scikit Image - Meijering Neuriteness Filter
- Scikit Image - Sato Filter
- Scikit Image - Sobel Filter
- Scikit Image - Farid Filter
- Scikit Image - Scharr Filter
- Scikit Image - Unsharp Mask Filter
- Scikit Image - Roberts Cross Operator
- Scikit Image - Lapalace Operator
- Scikit Image - Window Functions With Images
- Scikit Image - Thresholding
- Scikit Image - Applying Threshold
- Scikit Image - Otsu Thresholding
- Scikit Image - Local thresholding
- Scikit Image - Hysteresis Thresholding
- Scikit Image - Li thresholding
- Scikit Image - Multi-Otsu Thresholding
- Scikit Image - Niblack and Sauvola Thresholding
- Scikit Image - Restoring Images
- Scikit Image - Rolling-ball Algorithm
- Scikit Image - Denoising an Image
- Scikit Image - Wavelet Denoising
- Scikit Image - Non-local means denoising for preserving textures
- Scikit Image - Calibrating Denoisers Using J-Invariance
- Scikit Image - Total Variation Denoising
- Scikit Image - Shift-invariant wavelet denoising
- Scikit Image - Image Deconvolution
- Scikit Image - Richardson-Lucy Deconvolution
- Scikit Image - Recover the original from a wrapped phase image
- Scikit Image - Image Inpainting
- Scikit Image - Registering Images
- Scikit Image - Image Registration
- Scikit Image - Masked Normalized Cross-Correlation
- Scikit Image - Registration using optical flow
- Scikit Image - Assemble images with simple image stitching
- Scikit Image - Registration using Polar and Log-Polar
- Scikit Image - Feature Detection
- Scikit Image - Dense DAISY Feature Description
- Scikit Image - Histogram of Oriented Gradients
- Scikit Image - Template Matching
- Scikit Image - CENSURE Feature Detector
- Scikit Image - BRIEF Binary Descriptor
- Scikit Image - SIFT Feature Detector and Descriptor Extractor
- Scikit Image - GLCM Texture Features
- Scikit Image - Shape Index
- Scikit Image - Sliding Window Histogram
- Scikit Image - Finding Contour
- Scikit Image - Texture Classification Using Local Binary Pattern
- Scikit Image - Texture Classification Using Multi-Block Local Binary Pattern
- Scikit Image - Active Contour Model
- Scikit Image - Canny Edge Detection
- Scikit Image - Marching Cubes
- Scikit Image - Foerstner Corner Detection
- Scikit Image - Harris Corner Detection
- Scikit Image - Extracting FAST Corners
- Scikit Image - Shi-Tomasi Corner Detection
- Scikit Image - Haar Like Feature Detection
- Scikit Image - Haar Feature detection of coordinates
- Scikit Image - Hessian matrix
- Scikit Image - ORB feature Detection
- Scikit Image - Additional Concepts
- Scikit Image - Render text onto an image
- Scikit Image - Face detection using a cascade classifier
- Scikit Image - Face classification using Haar-like feature descriptor
- Scikit Image - Visual image comparison
- Scikit Image - Exploring Region Properties With Pandas
Scikit Image − Sato Filter
The Sato tubeness filter is an image processing algorithm designed to enhance ridge-like structures in images. The structures include objects like blood vessels, neurites, and other tube-like structures. The filter was introduced by Sato et al. in 1998, the primary purpose of the Sato tubeness filter is to enhance and highlight ridge-like structures within images. The Sato tubeness filter is often considered a faster alternative to another popular vessel enhancement filter called the Frangi filter.
The Sato tubeness filter belongs to a class of ridge filters that rely on the eigenvalues of the Hessian matrix of image intensities to identify ridge structures.
The scikit image library provides the sato() function in the filters module to apply the Sato tubeness filter on images.
Using the skimage.filters.sato() function
The filters.sato() function is used to filter an image with the Sato tubeness filter. The Sato tubeness filter is designed for detecting continuous ridges in an image. It is only defined for 2-D and 3-D images. It calculates the eigenvectors of the Hessian matrix to analyze the local structure of an image region.
Syntax
Following is the syntax of this function −
skimage.filters.sato(image, sigmas=range(1, 10, 2), black_ridges=True, mode='reflect', cval=0)
Parameters
The function accepts the following parameters −
- Image ((N, M[, P]) ndarray): This parameter is the input image on which the Sato tubeness filter will be applied.
- Sigmas (iterable of floats, optional): Specify the scales of the filter.
- Black_ridges (boolean, optional): The default value is True, determining whether the filter detects black ridges (True) or white ridges (False).
- Mode (string, optional): This parameter specifies how to handle values outside the image borders, with options like 'constant', 'reflect', 'wrap', 'nearest', or 'mirror'.
- Cval (float, optional): This is used in conjunction with mode 'constant' to specify the value outside the image boundaries.
Return value
The function then returns a filtered image represented as a NumPy ndarray. The result is the maximum value of pixels across all the scales, which highlights the detected ridges or structures in the image.
Example
This example applies the skimage.filters.sato() function on an image with its default parameter values −
import matplotlib.pyplot as plt from skimage.filters import sato from skimage import io, color # Load the input image in_image = io.imread('Images/tree.jpg') x_0 = 250 y_0 = 100 width = 250 height = 150 # Crop the image to the specified region and convert it to grayscale image = color.rgb2gray(in_image[y_0:(y_0 + height), x_0:(x_0 + width)]) # Apply the Sato filter with the default values filtered_image = sato(image) # Plot the original and filtered images fig, axes = plt.subplots(1, 2, figsize=(10, 5)) ax = axes.ravel() # Display the Original Image ax[0].imshow(image, cmap='gray') ax[0].set_title('Original Image') ax[0].axis('off') # Display the Sato Filter Result ax[1].imshow(filtered_image, cmap='gray') ax[1].axis('off') ax[1].set_title('Sato Filter Result') plt.tight_layout() plt.show()
Output
Example
The following example demonstrates the use of the Sato tubeness filter using the skimage.filters.sato() function on an image with different sigmas to enhance and visualize tube-like structures in an image −
from skimage import io from skimage import color from skimage.filters import sato import matplotlib.pyplot as plt # Load the input image in_image = io.imread('Images/tree.jpg') x_0 = 250 y_0 = 100 width = 250 height = 150 # Crop the image to the specified region and convert it to grayscale image = color.rgb2gray(in_image[y_0:(y_0 + height), x_0:(x_0 + width)]) # Apply the Sato tubeness filter with black_ridges=True result_black_ridges = sato(image, black_ridges=True, sigmas=[1]) result_black_ridges_sigmas = sato(image, black_ridges=True, sigmas=range(1, 5)) # Apply the Sato tubeness filter with black_ridges=False result_white_ridges = sato(image, black_ridges=False, sigmas=[1]) result_white_ridges_sigmas = sato(image, black_ridges=False, sigmas=range(1, 5)) # Plot the original, filtered (both black_ridges=True and black_ridges=False) images fig, axes = plt.subplots(2, 3, figsize=(10, 5)) ax = axes.ravel() # Display the Original Image ax[0].imshow(image, cmap='gray') ax[0].set_title('Original Image') ax[0].axis('off') # Display the Result with black_ridges=True ax[1].imshow(result_black_ridges, cmap='gray') ax[1].axis('off') ax[1].set_title('Sato Filter (black_ridges=True)\n \N{GREEK SMALL LETTER SIGMA}=[1]') # Display the Result with black_ridges=False ax[2].imshow(result_white_ridges, cmap='gray') ax[2].axis('off') ax[2].set_title('Sato Filter (black_ridges=False)\n \N{GREEK SMALL LETTER SIGMA} =[1]') # Display the Original Image again (for the second row) ax[3].imshow(image, cmap='gray') ax[3].axis('off') ax[3].set_title('Original Image') # Display the Result with black_ridges=True ax[4].imshow(result_black_ridges_sigmas, cmap='gray') ax[4].axis('off') ax[4].set_title('Sato Filter (black_ridges=True)\n \N{GREEK SMALL LETTER SIGMA} =[1,2,3,4]') # Display the Result with black_ridges=False ax[5].imshow(result_white_ridges_sigmas, cmap='gray') ax[5].axis('off') ax[5].set_title('Sato Filter (black_ridges=False)\n \N{GREEK SMALL LETTER SIGMA} =[1,2,3,4]') plt.tight_layout() plt.show()