评估分割指标

在尝试不同的分割方法时，你如何知道哪一种是最好的？如果你有一个 ground truth 或 gold standard 分割，你可以使用各种指标来检查每种自动化方法与真实情况的接近程度。在这个例子中，我们使用一个易于分割的图像作为示例，来说明如何解释各种分割指标。我们将使用调整后的Rand误差和信息变异作为示例指标，并观察 *过度分割*（将真实段落分割成过多子段落）和 *欠分割*（将不同的真实段落合并成一个段落）如何影响不同的评分。

import numpy as np
import matplotlib.pyplot as plt
from scipy import ndimage as ndi

from skimage import data
from skimage.metrics import adapted_rand_error, variation_of_information
from skimage.filters import sobel
from skimage.measure import label
from skimage.util import img_as_float
from skimage.feature import canny
from skimage.morphology import remove_small_objects
from skimage.segmentation import (
    morphological_geodesic_active_contour,
    inverse_gaussian_gradient,
    watershed,
    mark_boundaries,
)

image = data.coins()

首先，我们生成真实的分割。对于这张简单的图像，我们知道确切的函数和参数将产生完美的分割。在实际场景中，通常您会通过手动注释或“绘制”分割来生成地面实况。

elevation_map = sobel(image)
markers = np.zeros_like(image)
markers[image < 30] = 1
markers[image > 150] = 2
im_true = watershed(elevation_map, markers)
im_true = ndi.label(ndi.binary_fill_holes(im_true - 1))[0]

接下来，我们创建三个具有不同特征的分段。第一个使用 skimage.segmentation.watershed() 并带有 compactness，这是一个有用的初始分段，但作为最终结果来说过于精细。我们将看到这如何导致过分割指标的急剧上升。

edges = sobel(image)
im_test1 = watershed(edges, markers=468, compactness=0.001)

下一个方法使用Canny边缘滤波器，skimage.feature.canny()。这是一个非常好的边缘检测器，能给出平衡的结果。

edges = canny(image)
fill_coins = ndi.binary_fill_holes(edges)
im_test2 = ndi.label(remove_small_objects(fill_coins, 21))[0]

最后，我们使用形态学测地活动轮廓，skimage.segmentation.morphological_geodesic_active_contour()，这种方法通常能产生良好的结果，但需要很长时间才能收敛到一个好的答案。我们有意识地在100次迭代后截断过程，因此最终结果是*欠分割*的，这意味着许多区域被合并为一个段。我们将在分割指标上看到相应的效果。

image = img_as_float(image)
gradient = inverse_gaussian_gradient(image)
init_ls = np.zeros(image.shape, dtype=np.int8)
init_ls[10:-10, 10:-10] = 1
im_test3 = morphological_geodesic_active_contour(
    gradient,
    num_iter=100,
    init_level_set=init_ls,
    smoothing=1,
    balloon=-1,
    threshold=0.69,
)
im_test3 = label(im_test3)

method_names = [
    'Compact watershed',
    'Canny filter',
    'Morphological Geodesic Active Contours',
]
short_method_names = ['Compact WS', 'Canny', 'GAC']

precision_list = []
recall_list = []
split_list = []
merge_list = []
for name, im_test in zip(method_names, [im_test1, im_test2, im_test3]):
    error, precision, recall = adapted_rand_error(im_true, im_test)
    splits, merges = variation_of_information(im_true, im_test)
    split_list.append(splits)
    merge_list.append(merges)
    precision_list.append(precision)
    recall_list.append(recall)
    print(f'\n## Method: {name}')
    print(f'Adapted Rand error: {error}')
    print(f'Adapted Rand precision: {precision}')
    print(f'Adapted Rand recall: {recall}')
    print(f'False Splits: {splits}')
    print(f'False Merges: {merges}')

fig, axes = plt.subplots(2, 3, figsize=(9, 6), constrained_layout=True)
ax = axes.ravel()

ax[0].scatter(merge_list, split_list)
for i, txt in enumerate(short_method_names):
    ax[0].annotate(txt, (merge_list[i], split_list[i]), verticalalignment='center')
ax[0].set_xlabel('False Merges (bits)')
ax[0].set_ylabel('False Splits (bits)')
ax[0].set_title('Split Variation of Information')

ax[1].scatter(precision_list, recall_list)
for i, txt in enumerate(short_method_names):
    ax[1].annotate(txt, (precision_list[i], recall_list[i]), verticalalignment='center')
ax[1].set_xlabel('Precision')
ax[1].set_ylabel('Recall')
ax[1].set_title('Adapted Rand precision vs. recall')
ax[1].set_xlim(0, 1)
ax[1].set_ylim(0, 1)

ax[2].imshow(mark_boundaries(image, im_true))
ax[2].set_title('True Segmentation')
ax[2].set_axis_off()

ax[3].imshow(mark_boundaries(image, im_test1))
ax[3].set_title('Compact Watershed')
ax[3].set_axis_off()

ax[4].imshow(mark_boundaries(image, im_test2))
ax[4].set_title('Edge Detection')
ax[4].set_axis_off()

ax[5].imshow(mark_boundaries(image, im_test3))
ax[5].set_title('Morphological GAC')
ax[5].set_axis_off()

plt.show()

## Method: Compact watershed
Adapted Rand error: 0.5421684624091794
Adapted Rand precision: 0.2968781380256405
Adapted Rand recall: 0.9999664222191392
False Splits: 6.036024332525563
False Merges: 0.08258837118206537

## Method: Canny filter
Adapted Rand error: 0.0027247598212836177
Adapted Rand precision: 0.9946425605360896
Adapted Rand recall: 0.9999218934767155
False Splits: 0.20042002116129515
False Merges: 0.18076872508600772

## Method: Morphological Geodesic Active Contours
Adapted Rand error: 0.8346015951433162
Adapted Rand precision: 0.9191321393095933
Adapted Rand recall: 0.09087577915161697
False Splits: 0.6466330168716372
False Merges: 1.46562701331951