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preprocessing.py
----------------
Full radiology image preprocessing pipeline.
Covers ALL modalities: X-ray, CT, MRI, Ultrasound, PET, Mammography,
Fluoroscopy, Nuclear Medicine, DEXA, Angiography.
Supports DICOM (.dcm), PNG, JPG.
"""
import io
import logging
from pathlib import Path
from typing import Optional
import cv2
import numpy as np
from PIL import Image
logger = logging.getLogger(__name__)
class RadiologyPreprocessor:
TARGET_SIZE = (224, 224)
# ── Window presets for every modality + region combination ──────────
WINDOW_PRESETS = {
# X-RAY
"xray": {"wc": 40, "ww": 400},
"xray_chest": {"wc": -600, "ww": 1500},
"xray_abdomen": {"wc": 60, "ww": 400},
"xray_pelvis": {"wc": 40, "ww": 400},
"xray_spine": {"wc": 40, "ww": 400},
"xray_knee": {"wc": 40, "ww": 400},
"xray_shoulder": {"wc": 40, "ww": 400},
"xray_hand": {"wc": 40, "ww": 350},
"xray_foot": {"wc": 40, "ww": 350},
"xray_wrist": {"wc": 40, "ww": 350},
"xray_ankle": {"wc": 40, "ww": 350},
"xray_hip": {"wc": 40, "ww": 400},
"xray_skull": {"wc": 35, "ww": 80},
"xray_neck": {"wc": 40, "ww": 300},
# CT
"ct": {"wc": 40, "ww": 400},
"ct_chest": {"wc": -600, "ww": 1500},
"ct_abdomen": {"wc": 50, "ww": 350},
"ct_pelvis": {"wc": 50, "ww": 350},
"ct_brain": {"wc": 35, "ww": 80},
"ct_brain_bone": {"wc": 400, "ww": 2000},
"ct_brain_subdural": {"wc": 75, "ww": 215},
"ct_spine": {"wc": 400, "ww": 1800},
"ct_spine_soft": {"wc": 50, "ww": 350},
"ct_liver": {"wc": 60, "ww": 160},
"ct_lung": {"wc": -600, "ww": 1500},
"ct_mediastinum": {"wc": 50, "ww": 350},
"ct_bone": {"wc": 400, "ww": 1800},
"ct_angiography": {"wc": 300, "ww": 600},
"ct_cardiac": {"wc": 50, "ww": 350},
"ct_neck": {"wc": 50, "ww": 350},
"ct_kidney": {"wc": 50, "ww": 350},
"ct_pancreas": {"wc": 50, "ww": 250},
"ct_adrenal": {"wc": 35, "ww": 150},
# MRI
"mri": {"wc": 128, "ww": 256},
"mri_brain": {"wc": 128, "ww": 256},
"mri_spine": {"wc": 128, "ww": 256},
"mri_knee": {"wc": 128, "ww": 256},
"mri_shoulder": {"wc": 128, "ww": 256},
"mri_abdomen": {"wc": 128, "ww": 256},
"mri_pelvis": {"wc": 128, "ww": 256},
"mri_cardiac": {"wc": 128, "ww": 256},
"mri_breast": {"wc": 128, "ww": 256},
"mri_liver": {"wc": 128, "ww": 256},
"mri_prostate": {"wc": 128, "ww": 256},
"mri_neck": {"wc": 128, "ww": 256},
"mri_hip": {"wc": 128, "ww": 256},
"mri_ankle": {"wc": 128, "ww": 256},
"mri_wrist": {"wc": 128, "ww": 256},
"mri_fetal": {"wc": 128, "ww": 256},
# ULTRASOUND
"ultrasound": {"wc": 128, "ww": 256},
"ultrasound_abdomen": {"wc": 128, "ww": 256},
"ultrasound_pelvis": {"wc": 128, "ww": 256},
"ultrasound_thyroid": {"wc": 128, "ww": 256},
"ultrasound_breast": {"wc": 128, "ww": 256},
"ultrasound_cardiac": {"wc": 128, "ww": 256},
"ultrasound_obstetric": {"wc": 128, "ww": 256},
"ultrasound_vascular": {"wc": 128, "ww": 256},
"ultrasound_neck": {"wc": 128, "ww": 256},
"ultrasound_liver": {"wc": 128, "ww": 256},
"ultrasound_kidney": {"wc": 128, "ww": 256},
"ultrasound_testicular": {"wc": 128, "ww": 256},
"ultrasound_neonatal": {"wc": 128, "ww": 256},
# PET / NUCLEAR MEDICINE
"pet": {"wc": 128, "ww": 256},
"pet_brain": {"wc": 128, "ww": 256},
"pet_body": {"wc": 128, "ww": 256},
"nuclear medicine": {"wc": 128, "ww": 256},
"spect": {"wc": 128, "ww": 256},
# MAMMOGRAPHY
"mammography": {"wc": 1500, "ww": 6000},
"mammography_breast": {"wc": 1500, "ww": 6000},
# FLUOROSCOPY
"fluoroscopy": {"wc": 40, "ww": 400},
"fluoroscopy_gi": {"wc": 40, "ww": 400},
# ANGIOGRAPHY
"angiography": {"wc": 300, "ww": 600},
"angiography_cardiac": {"wc": 300, "ww": 600},
"angiography_cerebral": {"wc": 300, "ww": 600},
# DEXA (Bone Density)
"dexa": {"wc": 128, "ww": 256},
# Default fallback
"default": {"wc": 127, "ww": 255},
}
# ── Modality-specific enhancement settings ───────────────────────────
ENHANCEMENT_SETTINGS = {
"xray": {"clahe": True, "clahe_clip": 3.0, "denoise": False},
"ct": {"clahe": False, "clahe_clip": 2.0, "denoise": False},
"mri": {"clahe": True, "clahe_clip": 2.0, "denoise": True},
"ultrasound": {"clahe": True, "clahe_clip": 4.0, "denoise": True},
"pet": {"clahe": False, "clahe_clip": 2.0, "denoise": False},
"mammography": {"clahe": True, "clahe_clip": 2.5, "denoise": True},
"fluoroscopy": {"clahe": True, "clahe_clip": 3.0, "denoise": False},
"angiography": {"clahe": True, "clahe_clip": 2.0, "denoise": False},
"dexa": {"clahe": False, "clahe_clip": 2.0, "denoise": False},
"default": {"clahe": True, "clahe_clip": 2.0, "denoise": False},
}
def __init__(self, modality: str = "xray", body_region: str = "chest"):
self.modality = modality.lower().replace("-", " ").replace("_", " ")
self.body_region = body_region.lower().replace("-", " ")
self._window_preset = self._select_window_preset()
self._enhancement = self._select_enhancement()
# ------------------------------------------------------------------
# Public API
# ------------------------------------------------------------------
def preprocess(self, file_bytes: bytes, filename: str) -> np.ndarray:
"""Returns float32 numpy array (3, 224, 224) normalised to [0, 1]."""
ext = Path(filename).suffix.lower()
if ext == ".dcm":
image = self._load_dicom(file_bytes)
elif ext in {".png", ".jpg", ".jpeg"}:
image = self._load_standard_image(file_bytes)
else:
raise ValueError(f"Unsupported format: {ext}. Use .dcm, .png, or .jpg")
image = self._apply_modality_normalisation(image)
image = self._apply_enhancement(image)
image = self._resize(image)
return self._to_tensor(image)
def preprocess_pil(self, file_bytes: bytes, filename: str) -> Image.Image:
"""Returns a PIL Image for model input."""
tensor = self.preprocess(file_bytes, filename)
arr = (tensor.transpose(1, 2, 0) * 255).astype(np.uint8)
return Image.fromarray(arr)
# ------------------------------------------------------------------
# Private
# ------------------------------------------------------------------
def _select_window_preset(self) -> dict:
# Try modality_region key first
mod_key = self.modality.replace(" ", "_")
reg_key = self.body_region.replace(" ", "_")
combined = f"{mod_key}_{reg_key}"
if combined in self.WINDOW_PRESETS:
return self.WINDOW_PRESETS[combined]
if mod_key in self.WINDOW_PRESETS:
return self.WINDOW_PRESETS[mod_key]
return self.WINDOW_PRESETS["default"]
def _select_enhancement(self) -> dict:
mod_key = self.modality.replace(" ", "_").split("_")[0]
return self.ENHANCEMENT_SETTINGS.get(
mod_key, self.ENHANCEMENT_SETTINGS["default"]
)
def _load_dicom(self, file_bytes: bytes) -> np.ndarray:
try:
import pydicom
except ImportError:
raise RuntimeError("Install pydicom: pip install pydicom")
ds = pydicom.dcmread(io.BytesIO(file_bytes))
pixel_array = ds.pixel_array.astype(np.float32)
slope = float(getattr(ds, "RescaleSlope", 1))
intercept = float(getattr(ds, "RescaleIntercept", 0))
pixel_array = pixel_array * slope + intercept
# Try DICOM tags first, fall back to preset
wc = float(getattr(ds, "WindowCenter",
self._window_preset["wc"]))
ww = float(getattr(ds, "WindowWidth",
self._window_preset["ww"]))
try:
import pydicom.multival
if isinstance(wc, pydicom.multival.MultiValue):
wc = float(wc[0])
if isinstance(ww, pydicom.multival.MultiValue):
ww = float(ww[0])
except Exception:
pass
return self._apply_windowing(pixel_array, wc, ww)
def _load_standard_image(self, file_bytes: bytes) -> np.ndarray:
arr = np.frombuffer(file_bytes, dtype=np.uint8)
img = cv2.imdecode(arr, cv2.IMREAD_GRAYSCALE)
if img is None:
raise ValueError("Could not decode image.")
return img.astype(np.float32)
@staticmethod
def _apply_windowing(pixel_array, wc, ww) -> np.ndarray:
lower = wc - ww / 2
upper = wc + ww / 2
windowed = np.clip(pixel_array, lower, upper)
windowed = (windowed - lower) / (ww + 1e-8)
return (windowed * 255).astype(np.float32)
def _apply_modality_normalisation(self, image: np.ndarray) -> np.ndarray:
p2, p98 = np.percentile(image, [2, 98])
image = np.clip(image, p2, p98)
image = (image - p2) / (p98 - p2 + 1e-8) * 255.0
return image.astype(np.float32)
def _apply_enhancement(self, image: np.ndarray) -> np.ndarray:
"""Apply CLAHE and/or denoising based on modality."""
img_uint8 = np.clip(image, 0, 255).astype(np.uint8)
if self._enhancement.get("denoise"):
img_uint8 = cv2.fastNlMeansDenoising(img_uint8, h=10)
if self._enhancement.get("clahe"):
clip = self._enhancement.get("clahe_clip", 2.0)
clahe = cv2.createCLAHE(clipLimit=clip, tileGridSize=(8, 8))
img_uint8 = clahe.apply(img_uint8)
return img_uint8.astype(np.float32)
def _resize(self, image: np.ndarray) -> np.ndarray:
h, w = self.TARGET_SIZE
return cv2.resize(image, (w, h), interpolation=cv2.INTER_LANCZOS4)
def _to_tensor(self, image: np.ndarray) -> np.ndarray:
normalised = image / 255.0
return np.stack([normalised, normalised, normalised], axis=0).astype(np.float32)
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