Image feature extraction inside region-of-interest
Reason this release was yanked:
GLSZM connectivity issues
Project description
Image Feature Extraction in Region-of-Interest
A collection of python functions for feature extraction. The features are calculated inside a Region of Interest (ROI) and not for the whole image: the image is actually a polygon.
1. Features
1.1 Textural Features
- First Order Statistics/Statistical Features (FOS/SF)
- Gray Level Co-occurence Matrix (GLCM/SGLDM)
- Gray Level Difference Statistics (GLDS)
- Neighborhood Gray Tone Difference Matrix (NGTDM)
- Statistical Feature Matrix (SFM)
- Law's Texture Energy Measures (LTE/TEM)
- Fractal Dimension Texture Analysis (FDTA)
- Gray Level Run Length Matrix (GLRLM)
- Fourier Power Spectrum (FPS)
- Shape Parameters
- Gray Level Size Zone Matrix (GLSZM)
- Higher Order Spectra (HOS)
- Local Binary Pattern (LPB)
1.2 Morphological Features
- Grayscale Morphological Analysis
- Multilevel Binary Morphological Analysis
1.3 Histogram Based Features
- Histogram
- Multi-region histogram
- Correlogram
1.4 Multi-scale Features
- Fractal Dimension Texture Analysis (FDTA)
- Amplitude Modulation – Frequency Modulation (AM-FM)
- Discrete Wavelet Transform (DWT)
- Stationary Wavelet Transform (SWT)
- Wavelet Packets (WP)
- Gabor Transform (GT)
1.5 Other Features
- Zernikes’ Moments
- Hu’s Moments
- Threshold Adjacency Matrix (TAS)
- Histogram of Oriented Gradients (HOG)
2. Use
Download pyfeats using
pip install pyfeats
Import pyfeats using
import pyfeats
3. How to use each feature set
For the following sections, assume
- f is a grayscale image as a numpy ndarray,
- mask is an image as a numpy ndarray but with 2 values: 0 (zero) and 1 (one) with 1 indicating the ROI, where the features shall be calculated (values outside ROI are ignored),
- perimeter is like mask but indicates the perimeter of the ROI.
3.1 Textural Features
3.1.1 First Order Statistics/Statistical Features (FOS/SF)
features, labels = fos(f, mask)
'''
Parameters
----------
f : numpy ndarray
Image of dimensions N1 x N2.
mask : numpy ndarray
Mask image N1 x N2 with 1 if pixels belongs to ROI, 0 else. Give None
if you want to consider ROI the whole image.
Returns
-------
features : numpy ndarray
Feature set as defined in theory.
labels : list
Labels of features.
'''
3.1.2 Gray Level Co-occurence Matrix (GLCM/SGLDM)
features_mean, features_range, labels_mean, labels_range = glcm_features(f, ignore_zeros)
'''
Parameters
----------
f : numpy ndarray
Image of dimensions N1 x N2.
ignore_zeros : int, optional
Ignore zeros in image f. The default is True.
Returns
-------
features_mean : numpy ndarray
Haralick's features, mean
features_range : numpy ndarray
Haralick's features, same as before but range
labels_mean : list
Labels of features_mean.
labels_range: list
Labels of features_range.
'''
3.1.3 Gray Level Difference Statistics (GLDS)
features, labels = glds_features(f, mask, Dx, Dy)
'''
Parameters
----------
f : numpy ndarray
Image of dimensions N1 x N2.
mask : numpy ndarray
Mask image N1 x N2 with 1 if pixels belongs to ROI, 0 else.
Dx : int, optional
Array with X-coordinates of vectors denoting orientation. The default
is [0,1,1,1].
Dy : int, optional
Array with Y-coordinates of vectors denoting orientation. The default
is [1,1,0,-1].
Returns
-------
features : numpy ndarray
Feature set as defined in theory.
labels : list
Labels of features.
'''
3.1.4 Neighborhood Gray Tone Difference Matrix (NGTDM)
features, labels = ngtdm_features(f, mask, d)
'''
Parameters
----------
f : numpy ndarray
Image of dimensions N1 x N2.
mask : numpy ndarray
Mask image N1 x N2 with 1 if pixels belongs to ROI, 0 else. Give None
if you want to consider ROI the whole image.
d : int, optional
Distance for NGTDM. Default is 1.
Returns
-------
features : numpy ndarray
Feature set as defined in theory.
labels : list
Labels of features.
'''
3.1.5 Statistical Feature Matrix (SFM)
features, labels = sfm_features(f, mask, Lr, Lc)
'''
Parameters
----------
f : numpy ndarray
Image of dimensions N1 x N2.
mask : numpy ndarray
Mask image N1 x N2 with 1 if pixels belongs to ROI, 0 else. Give None
if you want to consider ROI the whole image.
Lr : int, optional
Parameters of SFM. The default is 4.
Lc : int, optional
Parameters of SFM. The default is 4.
Returns
-------
features : numpy ndarray
Feature set as defined in theory.
labels : list
Labels of features.
'''
3.1.6 Law's Texture Energy Measures (LTE/TEM)
features, labels = lte_measures(f, mask, l)
'''
Parameters
----------
f : numpy ndarray
Image of dimensions N1 x N2.
mask : numpy ndarray
Mask image N1 x N2 with 1 if pixels belongs to ROI, 0 else. Give None
if you want to consider ROI the whole image.
l : int, optional
Law's mask size. The default is 7.
Returns
-------
features : numpy ndarray
Feature set as defined in theory.
labels : list
Labels of features.
'''
3.1.7 Fractal Dimension Texture Analysis (FDTA)
h, labels = fdta(f, mask, s)
'''
Parameters
----------
f : numpy ndarray
Image of dimensions N1 x N2.
mask : numpy ndarray
Mask image N1 x N2 with 1 if pixels belongs to ROI, 0 else. Give None
if you want to consider ROI the whole image.
s : int, optional
max resolution to calculate Hurst coefficients. The default is 3
Returns
-------
h : numpy ndarray
Hurst coefficients.
labels : list
Labels of h.
'''
3.1.8 Gray Level Run Length Matrix (GLRLM)
features, labels = glrlm_features(f, mask, Ng)
'''
Parameters
----------
f : numpy ndarray
Image of dimensions N1 x N2.
mask : numpy ndarray
Mask image N1 x N2 with 1 if pixels belongs to ROI, 0 else. Give None
if you want to consider ROI the whole image.
Ng : int, optional
Image number of gray values. The default is 256.
Returns
-------
features : numpy ndarray
Feature set as defined in theory.
labels : list
Labels of features.
'''
3.1.9 Fourier Power Spectrum (FPS)
features, labels = fps(f, mask)
'''
Parameters
----------
f : numpy ndarray
Image of dimensions N1 x N2.
mask : numpy ndarray
Mask image N1 x N2 with 1 if pixels belongs to ROI, 0 else. Give None
if you want to consider ROI the whole image.
Returns
-------
features : numpy ndarray
Feature set as defined in theory.
labels : list
Labels of features.
'''
3.1.10 Shape Parameters
features, labels = shape_parameters(f, mask, perimeter, pixels_per_mm2)
'''
Parameters
----------
f : numpy ndarray
Image of dimensions N1 x N2.
mask : numpy ndarray
Mask image N1 x N2 with 1 if pixels belongs to ROI, 0 else. Give None
if you want to consider ROI the whole image.
perimeter : numpy ndarray
Image N1 x N2 with 1 if pixels belongs to perimeter of ROI, 0 else.
pixels_per_mm2 : int, optional
Density of image f. The default is 1.
Returns
-------
features : numpy ndarray
Feature set as defined in theory.
labels : list
Labels of features.
'''
3.1.11 Gray Level Size Zone Matrix (GLSZM)
features, labels = glszm_features(f, mask)
'''
Parameters
----------
f : numpy ndarray
Image of dimensions N1 x N2.
mask : numpy ndarray
Mask image N1 x N2 with 1 if pixels belongs to ROI, 0 else.
Returns
-------
features : numpy ndarray
Feature set as defined in theory.
labels : list
Labels of features.
'''
3.1.12 Higher Order Spectra (HOS)
features, labels = hos_features(f, th)
'''
Parameters
----------
f : numpy ndarray
Image of dimensions N1 x N2.
th : list, optional
Angle to calculate Radon Transform. The default is [135,140].
Returns
-------
features : numpy ndarray
Entropy of bispectrum of radeon transform of image for each angle in theta.
labels : list
Labels of features.
'''
3.1.13 Local Binary Pattern (LPB)
features, labels = lbp_features(f, mask, P, R)
'''
Parameters
----------
f : numpy ndarray
Image of dimensions N1 x N2.
mask : numpy ndarray
Mask image N1 x N2 with 1 if pixels belongs to ROI, 0 else. Give None
if you want to consider ROI the whole image.
P : list, optional
Number of points in neighborhood. The default is [8,16,24].
R : list, optional
Radius/Radii. The default is [1,2,3].
Returns
-------
features : numpy ndarray
Energy and entropy of LBP image (2 x 1).
labels : list
Labels of features.
'''
3.2 Morphological Features
3.2.1 Gray-scale Morphological Analysis
pdf, cdf = grayscale_morphology_features(f, N)
'''
Parameters
----------
f : numpy ndarray
Image of dimensions N1 x N2.
N : np.array, optional
Maximum number of scales. The default is 30.
Returns
-------
pdf : numpy ndarray
Probability density function (pdf) of pattern spectrum.
cdf : numpy ndarray
Cumulative density function (cdf) of pattern spectrum.
'''
3.2.2 Multilevel Binary Morphological Analysis
pdf_L, pdf_M, pdf_H, cdf_L, cdf_M, cdf_H = multilevel_binary_morphology_features(img, mask, N, thresholds):
'''
Parameters
----------
img : numpy ndarray
Image of dimensions N1 x N2.
mask : numpy ndarray
Mask image N1 x N2 with 1 if pixels belongs to ROI, 0 else. Give None
if you want to consider ROI the whole image.
N : np.array, optional
Maximum number of scales. The default is 30.
thresholds: list, optional
Thresholds to get the 3 binary images. The default is [25, 50].
Returns
-------
pdf_L : numpy ndarray
Probability density function (pdf) of pattern spectrum for image L.
pdf_M : numpy ndarray
Probability density function (pdf) of pattern spectrum for image M.
pdf_H : numpy ndarray
Probability density function (pdf) of pattern spectrum for image H.
cdf_L : numpy ndarray
Cumulative density function (cdf) of pattern spectrum for image L.
cdf_M : numpy ndarray
Cumulative density function (cdf) of pattern spectrum for image M.
cdf_H : numpy ndarray
Cumulative density function (cdf) of pattern spectrum for image H.
'''
3.3 Histogram Based Features
3.3.1 Histogram
H, labels = histogram(f, mask, bins)
'''
Parameters
----------
f : numpy ndarray
Image of dimensions N1 x N2.
mask : numpy ndarray
Mask image N1 x N2 with 1 if pixels belongs to ROI, 0 else. Give None
if you want to consider ROI the whole image.
bins : int, optional
Bins for histogram. The default is 32.
Returns
-------
H : numpy ndarray
Histogram of image f for 256 gray levels.
labels : list
Labels of features, which are the bins' number.
'''
3.3.2 Multi-region histogram
features, labels = multiregion_histogram(f, mask, bins, num_eros, square_size)
'''
Parameters
----------
f : numpy ndarray
Image of dimensions N1 x N2.
mask : numpy ndarray
Mask image N1 x N2 with 1 if pixels belongs to ROI, 0 else. Give None
if you want to consider ROI the whole image.
bins : int, optional
Bins for histogram. Default is 32.
num_eros : int, optional
Times of erosion to be performed. Default is 3.
square_size : int, optional
Kernel where erosion is performed, here a squared. Default square's
size is 3.
Returns
-------
features : numpy ndarray
Histogram of f and f after erosions as a vector e.g. [32 x num_eros].
labels : list
Labels of features.
'''
3.3.3 Correlogram
Hd, Ht, labels = correlogram(f, mask, bins_digitize, bins_hist, flatten)
'''
Parameters
----------
f : numpy ndarray
Image of dimensions N1 x N2.
mask : numpy ndarray
Mask image N1 x N2 with 1 if pixels belongs to ROI, 0 else. Give None
if you want to consider ROI the whole image.
bins_digitize : int, optional
Number of bins for discrete distances and thetas. The default is 32.
bins_hist : int, optional
Number of bins for histogram. The default is 32.
flatten : bool, optional
Return correlogram as 1d array if True or 2d array if False. The
default is False.
Returns
-------
Hd : numpy ndarray
Correlogram for distance.
Ht : numpy ndarray
Correlogram for angles.
labels : list
Labels of features.
'''
3.4 Multi-scale Features
3.4.1 Fractal Dimension Texture Analysis (FDTA)
h, labels = fdta(f, mask, s)
'''
Parameters
----------
f : numpy ndarray
Image of dimensions N1 x N2.
mask : numpy ndarray
Mask image N1 x N2 with 1 if pixels belongs to ROI, 0 else. Give None
if you want to consider ROI the whole image.
s : int, optional
max resolution to calculate Hurst coefficients. The default is 3
Returns
-------
h : numpy ndarray
Hurst coefficients.
labels : list
Labels of h.
'''
3.4.2 Amplitude Modulation – Frequency Modulation (AM-FM)
features, labels = amfm_features(f, bins)
'''
Parameters
----------
f : numpy ndarray
Image of dimensions N1 x N2.
bins: int, optional
Bins for the calculated histogram. The default is 32.
Returns
-------
features : numpy ndarray
Histogram of IA, IP, IFx, IFy as a concatenated vector.
labels : list
Labels of features.
'''
3.4.3 Discrete Wavelet Transform (DWT)
features, labels = dwt_features(f, mask, wavelet, levels)
'''
Parameters
----------
f : numpy ndarray
Image of dimensions N1 x N2.
mask : numpy ndarray
Mask image N1 x N2 with 1 if pixels belongs to ROI, 0 else. Give None
if you want to consider ROI the whole image.
wavelet : str, optional
Filter to be used. Check pywt for filter families. The default is 'bior3.3'
levels : int, optional
Levels of decomposition. Default is 3.
Returns
-------
features : numpy ndarray
Mean and std of each detail image. Appromimation images are ignored.
labels : list
Labels of features.
'''
3.4.4 Stationary Wavelet Transform (SWT)
features, labels = swt_features(f, mask, wavelet, levels)
'''
Parameters
----------
f : numpy ndarray
Image of dimensions N1 x N2.
mask : numpy ndarray
Mask image N1 x N2 with 1 if pixels belongs to ROI, 0 else. Give None
if you want to consider ROI the whole image.
wavelet : str, optional
Filter to be used. Check pywt for filter families. The default is 'bior3.3'
levels : int, optional
Levels of decomposition. Default is 3.
Returns
-------
features : numpy ndarray
Mean and std of each detail image. Appromimation images are ignored.
labels : list
Labels of features.
'''
3.4.5 Wavelet Packets (WP)
features, labels = wp_features(f, mask, wavelet, maxlevel)
'''
Parameters
----------
f : numpy ndarray
Image of dimensions N1 x N2.
mask : numpy ndarray
Mask image N1 x N2 with 1 if pixels belongs to ROI, 0 else. Give None
if you want to consider ROI the whole image.
wavelet : str, optional
Filter to be used. Check pywt for filter families. The default is 'cof1'
maxlevel : int, optional
Levels of decomposition. Default is 3.
Returns
-------
features : numpy ndarray
Mean and std of each detail image. Appromimation images are ignored.
labels : list
Labels of features.
'''
3.4.6 Gabor Transform (GT)
features, labels = gt_features(f, mask, deg, freq)
'''
Parameters
----------
f : numpy ndarray
Image of dimensions N1 x N2.
mask : numpy ndarray
Mask image N1 x N2 with 1 if pixels belongs to ROI, 0 else. Give None
if you want to consider ROI the whole image.
deg: int, optinal
Quantized degrees. The default is 4 (0, 45, 90, 135 degrees)
freq: list, optional
frequency of the gabor kernel. The default is [0.05, 0.4]
Returns
-------
features : numpy ndarray
Mean and std for the resulted image: (f o gabor_filter)(x,y)
labels : list
Labels of features.
'''
3.5 Other Features
3.5.1 Zernikes’ Moments
features, labels = zernikes_moments(f, radius)
'''
Parameters
----------
f : numpy ndarray
Image of dimensions N1 x N2.
radius : int, optional
Radius to calculate Zernikes moments. The default is 9.
Returns
-------
features : numpy ndarray
Zernikes' moments.
labels : list
Labels of features.
'''
3.5.2 Hu’s Moments
features, labels = hu_moments(f)
'''
Parameters
----------
f : numpy ndarray
Image of dimensions N1 x N2.
Returns
-------
features : numpy ndarray
Hu's moments.
labels : list
Labels of features.
'''
3.5.3 Threshold Adjacency Matrix (TAS)
features, labels = tas_features(f)
'''
Parameters
----------
f : numpy ndarray
Image of dimensions N1 x N2.
Returns
-------
features : numpy ndarray
Feature values.
labels : list
Labels of features.
'''
3.5.4 Histogram of Oriented Gradients (HOG)
fd, labels = hog_features(f, ppc, cpb)
'''
Parameters
----------
f : numpy ndarray
Image of dimensions N1 x N2.
ppc : int, optional
Pixels per cell. The default is 8.
cpb : int, optional
Cells per block. The default is 3.
Returns
-------
fd : numpy ndarray
Histogram of Oriented Gradients flattened.
labels : list
Labels of features.
'''
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