"""Second-order texture features derived from the gray-level co-occurrence matrix."""
import re
from ._measurement_info import Entry
from ._tiers import DiscriminativeFeature
# ``scale`` is emitted with ``{scale:02d}`` (a *minimum* width), so scales >= 100
# render more than two digits — match ``\d{2,}`` so large GLCM offsets stay
# recognizable rather than silently degrading to unrecognized columns.
_TEXTURE_HEADER_RE = re.compile(
r"^(?P<cat>[A-Za-z0-9]+)_(?P<label>[^-]+)-(?:deg\d{3}|avg)-scale\d{2,}$"
)
[docs]
class TEXTURE(DiscriminativeFeature):
"""Second-order texture features derived from the gray-level co-occurrence matrix (GLCM).
All features assume normalized GLCMs computed at one or more pixel offsets and averaged
across directions unless otherwise noted. Values depend on quantization, window size,
and scale; interpret ranges comparatively within the same imaging setup.
Textures are calculated along the 0, 45, 90, and 135 degree axes across the surface
of an object. This is denoted by the axis placeholder. The texture is also computed
along different scales in the image, and this scale can be converted to real
distance measurements using the px-per-mm conversion. If your image's are from ExFAB
BioFoundry we provide this for you, otherwise it can be found using ImageJ. As an
example, a scale of 10 with 40 px-per-mm means that the measurement is the texture
measured across every 0.25 mm on the surface of an object.
Texture_<feature_name>-deg<axis>-scale<scale>
We also average the texture across all degrees to provide:
Texture_<feature_name>-avg-scale<scale>
"""
[docs]
@classmethod
def category(cls) -> str:
return "Texture"
ANGULAR_SECOND_MOMENT = Entry(
"AngularSecondMoment",
"""Angular second moment (energy / uniformity). Measures the degree of local homogeneity
(Σ p(i,j)²). High values → uniform texture (e.g., smooth, yeast-like colonies with consistent
mycelial density). Low values → heterogeneous surfaces (e.g., sectored, wrinkled, or mixed
sporulation zones). Reflects colony surface regularity rather than brightness.""",
)
CONTRAST = Entry(
"Contrast",
"""Contrast (local intensity variation; Σ (i–j)² p(i,j)). High values indicate strong gray-level
differences (e.g., sharply defined rings, radial sectors, raised or folded regions). Low values
indicate gradual tonal changes or uniformly pigmented colonies. Quantifies visual roughness
and zonation amplitude.""",
)
CORRELATION = Entry(
"Correlation",
"""Linear gray-level correlation between neighboring pixels. Positive, high values suggest
structured spatial dependence (e.g., oriented radial hyphae or concentric patterns); near-zero
values indicate uncorrelated, disordered growth (e.g., diffuse cottony mycelium). Sensitive to
illumination gradients and directional GLCM computation.""",
)
VARIANCE = Entry(
"HaralickVariance",
"""GLCM variance (Σ (i–μ)² p(i,j)). Captures spread of co-occurring gray-level pairs, distinct
from raw intensity variance. High values → complex, multi-zone textures with variable
hyphal/spore densities. Low values → consistent gray-level relationships and simpler colony
surfaces.""",
)
INVERSE_DIFFERENCE_MOMENT = Entry(
"InverseDifferenceMoment",
"""Homogeneity (Σ p(i,j) / (1 + (i–j)²)). High values → smooth, locally uniform textures
(e.g., glabrous colonies, uniform aerial mycelium). Low values → abrupt gray-level changes
(e.g., granular sporulation, wrinkled surfaces). Typically inversely correlated with Contrast.""",
)
SUM_AVERAGE = Entry(
"SumAverage",
"""Mean of gray-level sums (Σ k·p_{x+y}(k)). Reflects the average intensity combination of
neighboring pixels. In fungal colonies, can loosely parallel mean colony brightness when
illumination and exposure are controlled, but remains a second-order rather than first-order
intensity metric.""",
)
SUM_VARIANCE = Entry(
"SumVariance",
"""Variance of gray-level sum distribution. High values → heterogeneous brightness zones
(e.g., alternating dense/sparse or pigmented/non-pigmented regions). Low values → uniform
tone across the colony. Often correlated with Contrast; use comparatively within one setup.""",
)
SUM_ENTROPY = Entry(
"SumEntropy",
"""Entropy of the gray-level sum distribution. High values → diverse brightness combinations
and irregular zonation. Low values → repetitive or periodic brightness patterns (e.g., evenly
spaced rings). Indicates spatial unpredictability of summed intensities.""",
)
ENTROPY = Entry(
"Entropy",
"""Global GLCM entropy (–Σ p(i,j)·log p(i,j)). Measures total texture disorder and information
content. High values → complex, irregular colony surfaces (powdery, fuzzy, or sectored growth).
Low values → simple, smooth, predictable patterns (glabrous or uniform colonies). Sensitive to
gray-level quantization and image dynamic range.""",
)
DIFFERENCE_VARIANCE = Entry(
"DiffVariance",
"""Variance of gray-level difference distribution. High values → mixture of smooth and textured
regions (e.g., smooth margins with wrinkled centers). Low values → consistent edge content.
Highlights heterogeneity in edge magnitude across the colony.""",
)
DIFFERENCE_ENTROPY = Entry(
"DiffEntropy",
"""Entropy of gray-level difference distribution. High values → irregular, unpredictable
intensity transitions (e.g., random sporulation or uneven mycelial networks). Low values →
regular periodic transitions (e.g., concentric zonation). Reflects randomness of local contrast
rather than its magnitude.""",
)
IMC1 = Entry(
"InfoCorrelation1",
"""Information measure of correlation 1. Compares joint vs marginal entropies to quantify
mutual dependence between gray levels. Positive values → structured, predictable textures
(e.g., organized radial growth); near-zero → independence between adjacent regions.
Direction of sign varies with implementation.""",
)
IMC2 = Entry(
"InfoCorrelation2",
"""Information measure of correlation 2 (√[1 – exp(–2 (H_xy2–H_xy))]). Always ≥ 0.
Values approaching 1 → strong spatial dependence and organized architecture (e.g., symmetric
rings, radial structure). Values near 0 → random, independent patterns. Captures nonlinear
organization missed by linear correlation.""",
)
