| """Pixel-level edge-density tissue mask for HANCOCK TMA cores (finalized method). |
| Sobel gradient magnitude -> Gaussian local density -> threshold -> close -> keep |
| components>=1% -> fill solid -> erode (tight boundary that hugs tissue). |
| Validated on regular / irregular-fragmented / faint cores; removal 0-13% (outside-core debris). |
| """ |
| import numpy as np, cv2 |
|
|
|
|
| def tissue_mask_px(rgb, sigma=10, thr=2, close=31, erode=25, min_frac=0.01): |
| """rgb uint8 HxWx3 -> uint8 {0,1} tissue mask (whole core, debris/background excluded).""" |
| gray = cv2.cvtColor(rgb, cv2.COLOR_RGB2GRAY).astype(np.float32) |
| mag = cv2.magnitude(cv2.Sobel(gray, cv2.CV_32F, 1, 0, ksize=3), |
| cv2.Sobel(gray, cv2.CV_32F, 0, 1, ksize=3)) |
| m = (cv2.GaussianBlur(mag, (0, 0), sigma) > thr).astype(np.uint8) |
| m = cv2.morphologyEx(m, cv2.MORPH_CLOSE, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (close, close))) |
| nlab, lab, stats, _ = cv2.connectedComponentsWithStats(m, 8) |
| keep = np.zeros_like(m); minpx = min_frac * m.size |
| for i in range(1, nlab): |
| if stats[i, cv2.CC_STAT_AREA] >= minpx: keep[lab == i] = 1 |
| cnts, _ = cv2.findContours(keep, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) |
| filled = np.zeros_like(keep) |
| for c in cnts: |
| if cv2.contourArea(c) >= minpx: cv2.drawContours(filled, [c], -1, 1, -1) |
| if erode > 1: |
| filled = cv2.erode(filled, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (erode, erode))) |
| return filled |
|
|
|
|
| def cells_in_mask(mask, center_xy): |
| """center_xy (N,2) int -> bool (N,) whether each cell center is inside the tissue mask.""" |
| H, W = mask.shape |
| if len(center_xy) == 0: return np.zeros(0, bool) |
| xs = np.clip(center_xy[:, 0].astype(int), 0, W - 1) |
| ys = np.clip(center_xy[:, 1].astype(int), 0, H - 1) |
| return mask[ys, xs] > 0 |
|
|
