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b379113 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 | """Fundus preprocessing primitives.
The pipeline order matters and is not arbitrary:
crop_from_gray -> circle_crop -> ben_graham -> (CLAHE) -> resize
`crop_from_gray` first, because the black surround otherwise drags down every
subsequent statistic (the blur in `ben_graham`, the CLAHE histogram, the
illumination estimate). `ben_graham` before `resize`, because it is defined
relative to image radius and is cheapest to reason about at native scale.
Reference: Ben Graham's winning solution, Kaggle Diabetic Retinopathy 2015.
"""
from __future__ import annotations
import cv2
import numpy as np
__all__ = [
"crop_from_gray",
"circle_crop",
"ben_graham",
"apply_clahe",
"preprocess",
]
def crop_from_gray(image: np.ndarray, tol: int = 7) -> np.ndarray:
"""Trim the uninformative dark border around a fundus photograph.
Fundus cameras produce a bright circular retina on a black rectangle. The
black region carries no signal but skews global statistics, so it goes first.
Args:
image: HxWx3 uint8 RGB, or HxW uint8 grayscale.
tol: Intensity at or below which a pixel counts as background.
Returns:
The tightest crop containing above-threshold content. Returns the input
unchanged if the mask is empty (a fully dark frame), rather than raising
-- an all-dark image is a *quality* failure and is Stage 1's business,
not this function's.
"""
if image.ndim == 2:
mask = image > tol
if not mask.any():
return image
return image[np.ix_(mask.any(1), mask.any(0))]
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
mask = gray > tol
if not mask.any():
return image
rows, cols = mask.any(1), mask.any(0)
return image[np.ix_(rows, cols)]
def circle_crop(image: np.ndarray) -> np.ndarray:
"""Mask everything outside the inscribed circular field of view.
Standardises the FOV across cameras so the model cannot learn to exploit
corner artefacts that happen to correlate with a particular device.
"""
image = crop_from_gray(image)
h, w = image.shape[:2]
cx, cy = w // 2, h // 2
r = min(cx, cy)
mask = np.zeros((h, w), dtype=np.uint8)
cv2.circle(mask, (cx, cy), r, color=1, thickness=-1)
out = image * mask[..., None] if image.ndim == 3 else image * mask
return crop_from_gray(out)
def ben_graham(image: np.ndarray, sigma_scale: float = 30.0, weight: float = 4.0) -> np.ndarray:
"""Subtract local average colour (Ben Graham, Kaggle DR 2015).
out = weight*img - weight*GaussianBlur(img, sigma) + 128
The blur sigma is tied to *image radius*, not to a fixed pixel count, which
makes the normalisation scale-invariant -- important because fundus cameras
output wildly different resolutions.
This removes the between-camera and between-patient variation in overall
illumination and pigmentation while preserving the high-frequency detail
that lesions live in.
"""
radius = min(image.shape[:2]) / 2.0
sigma = max(radius / sigma_scale, 1.0)
blurred = cv2.GaussianBlur(image, (0, 0), sigmaX=sigma)
out = cv2.addWeighted(image, weight, blurred, -weight, 128)
return np.clip(out, 0, 255).astype(np.uint8)
def apply_clahe(image: np.ndarray, clip_limit: float = 2.0, tile_grid: int = 8) -> np.ndarray:
"""Contrast-limited adaptive histogram equalisation on the LAB L channel.
Operating on L rather than per-RGB-channel avoids the colour casts that
channel-wise CLAHE introduces.
Note: `clip_limit` is a genuine risk knob. Too high and sensor noise is
amplified into structures that look like microaneurysms. Ablate it; do not
assume 2.0 is right for your camera mix.
"""
lab = cv2.cvtColor(image, cv2.COLOR_RGB2LAB)
clahe = cv2.createCLAHE(clipLimit=clip_limit, tileGridSize=(tile_grid, tile_grid))
lab[..., 0] = clahe.apply(lab[..., 0])
return cv2.cvtColor(lab, cv2.COLOR_LAB2RGB)
def preprocess(
image: np.ndarray,
size: int = 512,
use_ben_graham: bool = True,
use_clahe: bool = False,
) -> np.ndarray:
"""Full preprocessing chain, resized to `size` x `size`.
`use_clahe` defaults to False deliberately: it is a hypothesis to be tested
in the ablation, not a default to be inherited. See docs/01_PROJECT_ANALYSIS.md
section 6, Stage 2.
"""
out = circle_crop(image)
if use_ben_graham:
out = ben_graham(out)
if use_clahe:
out = apply_clahe(out)
return cv2.resize(out, (size, size), interpolation=cv2.INTER_AREA)
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