| | import numpy as np |
| | import cv2 |
| | import pandas as pd |
| | from scipy import ndimage as ndi |
| | from skimage.segmentation import watershed |
| | from skimage.feature import peak_local_max |
| | from skimage.measure import label, regionprops |
| | from .config import Config |
| |
|
| | class ImageProcessor: |
| | @staticmethod |
| | def macenko_normalize(img, Io=240, alpha=1, beta=0.15): |
| | """Normalizes H&E staining appearance.""" |
| | try: |
| | HER = np.array([[0.650, 0.704, 0.286], [0.072, 0.990, 0.105], [0.268, 0.570, 0.776]]) |
| | h, w, c = img.shape |
| | img_flat = img.reshape((-1, 3)) |
| | OD = -np.log((img_flat.astype(float) + 1) / Io) |
| | |
| | ODhat = OD[np.all(OD > beta, axis=1)] |
| | if len(ODhat) < 10: return img |
| | |
| | eigvals, eigvecs = np.linalg.eigh(np.cov(ODhat.T)) |
| | That = ODhat.dot(eigvecs[:, 1:3]) |
| | phi = np.arctan2(That[:, 1], That[:, 0]) |
| | minPhi = np.percentile(phi, alpha) |
| | maxPhi = np.percentile(phi, 100 - alpha) |
| | |
| | vMin = eigvecs[:, 1:3].dot(np.array([(np.cos(minPhi), np.sin(minPhi))]).T) |
| | vMax = eigvecs[:, 1:3].dot(np.array([(np.cos(maxPhi), np.sin(maxPhi))]).T) |
| | |
| | if vMin[0] > vMax[0]: HE = np.array((vMin[:, 0], vMax[:, 0])).T |
| | else: HE = np.array((vMax[:, 0], vMin[:, 0])).T |
| | |
| | Y = np.reshape(OD, (-1, 3)).T |
| | C = np.linalg.lstsq(HE, Y, rcond=None)[0] |
| | maxC = np.array([1.9705, 1.0308]) |
| | |
| | Inorm = Io * np.exp(-np.dot(HER[:, 0:2], (C/maxC * maxC)[:, np.newaxis])) |
| | return np.clip(np.reshape(Inorm.T, (h, w, c)), 0, 255).astype(np.uint8) |
| | except: |
| | return img |
| |
|
| | @staticmethod |
| | def adaptive_watershed(pred_nuc, pred_con): |
| | """Separates touching cells using probability topography.""" |
| | nuc_mask = (pred_nuc > Config.NUC_THRESHOLD).astype(np.uint8) |
| | con_mask = (pred_con > Config.CON_THRESHOLD).astype(np.uint8) |
| | |
| | |
| | markers_raw = np.clip(nuc_mask - con_mask, 0, 1) |
| | kernel = np.ones((3,3), np.uint8) |
| | markers_clean = cv2.morphologyEx(markers_raw, cv2.MORPH_OPEN, kernel, iterations=1) |
| | |
| | |
| | distance = ndi.distance_transform_edt(markers_clean) |
| | coords = peak_local_max(distance, footprint=np.ones((5, 5)), labels=markers_clean, min_distance=5) |
| | mask = np.zeros(distance.shape, dtype=bool) |
| | mask[tuple(coords.T)] = True |
| | markers, _ = ndi.label(mask) |
| | |
| | |
| | return watershed(-distance, markers, mask=nuc_mask) |
| |
|
| | @staticmethod |
| | def calculate_morphometrics(label_mask): |
| | """Calculates biological features for each cell.""" |
| | regions = regionprops(label_mask) |
| | stats = [] |
| | for prop in regions: |
| | area = prop.area |
| | if area < 30: continue |
| | perimeter = prop.perimeter |
| | if perimeter == 0: continue |
| | |
| | |
| | circularity = (4 * np.pi * area) / (perimeter ** 2) |
| | aspect_ratio = prop.major_axis_length / (prop.minor_axis_length + 1e-5) |
| | |
| | stats.append({ |
| | 'Area': area, |
| | 'Perimeter': int(perimeter), |
| | 'Circularity': round(circularity, 3), |
| | 'Solidity': round(prop.solidity, 3), |
| | 'Aspect_Ratio': round(aspect_ratio, 2) |
| | }) |
| | return pd.DataFrame(stats) |
| | |
| | @staticmethod |
| | def calculate_entropy(prob_map): |
| | """Calculates Shannon Entropy (Uncertainty Map).""" |
| | prob_map = np.clip(prob_map, 1e-7, 1-1e-7) |
| | entropy = - (prob_map * np.log(prob_map) + (1-prob_map) * np.log(1-prob_map)) |
| | return entropy |
