import numpy as np import SimpleITK as sitk from scipy.ndimage import center_of_mass import matplotlib.pyplot as plt def azimuth_rotation(image, show_plt=False, save_plt=False, output_path=None): img = sitk.ReadImage(image, sitk.sitkUInt8) arr_zyx = sitk.GetArrayFromImage(img) # (z, y, x) max_proj = np.max(arr_zyx, axis=0) # -> (y, x) binary_proj = (max_proj > 0).astype(np.uint8) ys, xs = np.where(binary_proj > 0) if len(xs) < 10: raise ValueError("Not enough foreground points") # 2) centroid ←←← 這裡一定會定義 cx, cy cy = ys.mean() cx = xs.mean() centroid = np.array([cy, cx]) cy = ys.mean() cx = xs.mean() centroid = np.array([cy, cx]) y_min = ys.min() top_row_mask = (ys == y_min) xs_top_row = xs[top_row_mask] # 取這一排的中位數或平均值 x_center = int(np.median(xs_top_row)) # 或用 np.mean() top_point = (y_min, x_center) # print(f"最上排中心點: {top_point}") # 計算從 top_point 到 centroid 的向量 dy = cy - y_min # y 方向的變化 dx = cx - x_center # x 方向的變化 # 計算與 y 軸的夾角 # 注意：影像座標系中 y 軸向下，所以要特別處理 angle_rad = np.arctan2(dx, dy) # 弧度 angle_deg = np.degrees(angle_rad) # 轉成角度 if show_plt: fig = plt.figure(figsize=(12, 12)) # 視覺化時加上角度資訊 plt.imshow(binary_proj, cmap='gray') plt.scatter(x_center, y_min, c='red', s=60, label='Top') plt.scatter(cx, cy, c='yellow', s=60, label='Centroid') plt.plot([x_center, cx], [y_min, cy], 'c-', lw=2, label=f'Angle with y-axis: {angle_deg:.1f}°') plt.legend() plt.title("Top point + centroid-directed line") plt.axis("off") if save_plt: if output_path is None: output_path = "azimuth_rotation.png" fig.savefig(output_path, dpi=200, bbox_inches="tight") plt.show() plt.close(fig) return angle_deg def split_spine_anterior_posterior(image_path, center_mode='com'): """ 從 sagittal view 看，沿著 y 軸（前後方向）將脊椎切成前半部和後半部 Parameters: image_path (str): 影像路徑 center_mode (str): 'com' 使用質心的 y 座標，'image' 使用圖片中心的 y 座標 Returns: anterior, posterior: 前半部和後半部的 binary mask """ # Sagittal projection: 沿著 x 軸投影 -> (z, y) img = sitk.ReadImage(image_path, sitk.sitkUInt8) arr_zyx = sitk.GetArrayFromImage(img) # Sagittal projection max_proj_sagittal = np.max(arr_zyx, axis=2) binary_proj = (max_proj_sagittal > 0).astype(np.uint8) # 決定切割的 y 座標 if center_mode == 'com': cz, cy = center_of_mass(binary_proj) split_y = int(round(cy)) label = f'Center of Mass (y={split_y})' elif center_mode == 'image': split_y = binary_proj.shape[1] // 2 label = f'Image Center (y={split_y})' # 檢查是否為數字，且範圍在 0 到 1 之間 (不含邊界) elif isinstance(center_mode, (int, float)) and 0 < center_mode < 1: ys = np.where(binary_proj > 0)[1] if ys.size == 0: # 額外保險：如果投影是空的 split_y = binary_proj.shape[1] // 2 else: y_min, y_max = ys.min(), ys.max() split_y = int(round(y_min + center_mode * (y_max - y_min))) label = f'Custom Ratio {center_mode} (y={split_y})' else: raise ValueError("center_mode 必須是 'com'、'image' 或介於 0 到 1 之間的浮點數 (例如 0.8)") # 切割：anterior (y < split_y) 和 posterior (y >= split_y) anterior = binary_proj.copy() posterior = binary_proj.copy() anterior[:, :split_y] = 0 # 保留spine前半部(image後半部)（y >= split_y） posterior[:, split_y:] = 0 # 保留spine後半部(image前半部)（y < split_y） return anterior, posterior, binary_proj def analyze_vertebral_tilt_contour(image_path, edge_type='superior', show_plot=False, debug=False, save_plt=False, output_path=None): """ 通過椎體前緣輪廓分析傾斜（可選上或下終板） Parameters: edge_type: 'superior' 上終板, 'inferior' 下終板, 'both' 兩者都分析 """ # 切割出 anterior 部分 anterior, posterior, binary_proj = split_spine_anterior_posterior(image_path, center_mode='com') zs, ys = np.where(anterior > 0) if len(zs) == 0: return None from sklearn.linear_model import RANSACRegressor results = {} # === 根據 edge_type 決定要分析哪些邊 === edges_to_analyze = [] if edge_type == 'superior' or edge_type == 'both': edges_to_analyze.append('superior') if edge_type == 'inferior' or edge_type == 'both': edges_to_analyze.append('inferior') all_edge_points = {} all_inliers = {} all_outliers = {} all_slopes = {} all_intercepts = {} all_angles = {} for edge in edges_to_analyze: # 對每個 y，找對應的邊緣點 edge_points = [] unique_ys = np.unique(ys) for y in unique_ys: z_at_y = zs[ys == y] if edge == 'superior': z_edge = z_at_y.max() # 最上面的點（z 最小） else: # inferior z_edge = z_at_y.min() # 最下面的點（z 最大） edge_points.append([y, z_edge]) edge_points = np.array(edge_points) all_edge_points[edge] = edge_points if len(edge_points) < 10: continue # RANSAC 擬合 X = edge_points[:, 0].reshape(-1, 1) y_data = edge_points[:, 1] ransac = RANSACRegressor( residual_threshold=5.0, random_state=42 ) ransac.fit(X, y_data) inlier_mask = ransac.inlier_mask_ outlier_mask = ~inlier_mask edge_points_inliers = edge_points[inlier_mask] edge_points_outliers = edge_points[outlier_mask] all_inliers[edge] = edge_points_inliers all_outliers[edge] = edge_points_outliers # 獲取擬合結果 slope = ransac.estimator_.coef_[0] intercept = ransac.estimator_.intercept_ all_slopes[edge] = slope all_intercepts[edge] = intercept # 計算 R² y_pred = ransac.predict(edge_points_inliers[:, 0].reshape(-1, 1)) ss_res = np.sum((edge_points_inliers[:, 1] - y_pred) ** 2) ss_tot = np.sum((edge_points_inliers[:, 1] - np.mean(edge_points_inliers[:, 1])) ** 2) r_squared = 1 - (ss_res / ss_tot) if ss_tot > 0 else 0 # 計算傾斜角度 tilt_angle = np.degrees(np.arctan(slope)) all_angles[edge] = tilt_angle if debug: print(f"\n=== {edge.upper()} ENDPLATE ===") print(f"Total points: {len(edge_points)}") print(f"Inliers: {len(edge_points_inliers)}") print(f"Outliers: {len(edge_points_outliers)}") print(f"{edge.capitalize()} 終板傾斜角度: {tilt_angle:.2f}°") print(f"斜率: {slope:.4f}, R²: {r_squared:.4f}") results[edge] = { 'tilt_angle_deg': tilt_angle, 'slope': slope, 'intercept': intercept, 'r_squared': r_squared, 'n_inliers': len(edge_points_inliers), 'n_outliers': len(edge_points_outliers) } # Visualization if show_plot: n_edges = len(edges_to_analyze) fig, axes = plt.subplots(n_edges, 2, figsize=(16, 6*n_edges)) if n_edges == 1: axes = axes.reshape(1, -1) colors = {'superior': 'red', 'inferior': 'cyan'} for idx, edge in enumerate(edges_to_analyze): edge_points = all_edge_points[edge] edge_points_inliers = all_inliers[edge] edge_points_outliers = all_outliers[edge] slope = all_slopes[edge] intercept = all_intercepts[edge] tilt_angle = all_angles[edge] color = colors[edge] # 左圖：scatter plot ax_left = axes[idx, 0] if len(edge_points_outliers) > 0: ax_left.scatter(edge_points_outliers[:, 0], edge_points_outliers[:, 1], c='lightcoral', s=30, alpha=0.6, marker='x', label=f'Outliers ({len(edge_points_outliers)})', zorder=3) ax_left.scatter(edge_points_inliers[:, 0], edge_points_inliers[:, 1], c=color, s=20, alpha=0.7, label=f'Inliers ({len(edge_points_inliers)})', zorder=4) # 擬合線 y_line = np.array([edge_points[:, 0].min(), edge_points[:, 0].max()]) z_line = slope * y_line + intercept ax_left.plot(y_line, z_line, 'lime', linewidth=3, label=f'Angle: {tilt_angle:.1f}°\nR²: {results[edge]["r_squared"]:.3f}', zorder=5) ax_left.set_xlabel('y') ax_left.set_ylabel('z') ax_left.set_title(f'{edge.capitalize()} Endplate Analysis\nTilt: {tilt_angle:.1f}°') ax_left.invert_yaxis() ax_left.legend() ax_left.grid(True, alpha=0.3) # 右圖：原始影像 ax_right = axes[idx, 1] ax_right.imshow(anterior, cmap='gray', aspect='equal') if len(edge_points_outliers) > 0: ax_right.scatter(edge_points_outliers[:, 0], edge_points_outliers[:, 1], c='red', s=40, alpha=0.7, marker='x', label='Outliers', zorder=4) ax_right.scatter(edge_points_inliers[:, 0], edge_points_inliers[:, 1], c=color, s=25, alpha=0.8, label='Inliers', zorder=3) ax_right.plot(y_line, z_line, 'lime', linewidth=3, linestyle='--', label=f'{edge.capitalize()}: {tilt_angle:.1f}°', zorder=5) ax_right.set_title(f'Anterior Half - {edge.capitalize()} Edge') ax_right.set_xlabel('y') ax_right.set_ylabel('z') ax_right.invert_yaxis() ax_right.legend() plt.tight_layout() if save_plt: if output_path is None: output_path = "analyze_vertebral_tilt_contour.png" fig.savefig(output_path, dpi=200, bbox_inches="tight") plt.show() plt.close(fig) return results