python - 如何通过传统的计算机视觉方法对特定的头部 CT 区域进行分割和计算？

Question

我正在为计算机视觉 (CV) 课程生成材料，我想通过传统的 CV 技术计算这个突出显示部分的面积：

因此，我应用 Canny 来检测边缘，并使用 Circle Hough 变换试图找到相应的区域。这些是我的结果：

我尝试使用分水岭，将标记作为我找到的圆圈的中心，但没有成功。有谁知道我该如何继续或有其他想法？

这是代码：

import numpy as np
import matplotlib.pyplot as plt
import cv2 as cv
from skimage.feature import canny
from skimage.transform import hough_circle, hough_circle_peaks
from skimage.draw import circle_perimeter
from skimage.segmentation import watershed

import urllib.request

urllib.request.urlretrieve("https://github.com/LAVI-USP/SEL0339-SEL5886/raw/master/imagens/pratica_07/head_CT.tif", "head_CT.tif")

# Read image
img = cv.imread("head_CT.tif",-1)

# Edge detector
edges = canny(img, sigma=2.0, low_threshold=19, high_threshold=57)

# Hough_circle
hough_radii = np.arange(29, 32, 1)
hough_res = hough_circle(edges, hough_radii)
accums, cx, cy, radii = hough_circle_peaks(hough_res, hough_radii,total_num_peaks=4, min_xdistance=200,min_ydistance=200, threshold=0.25)

# Remove false-posite circle
sortX = np.argsort(cx)
cx = cx[sortX[:-1]]
cy = cy[sortX[:-1]]
radii = radii[sortX[:-1]]

# Draw red circles
img_rgb = np.tile(np.expand_dims(img,axis=-1),(1,1,3),)
for center_y, center_x, radius in zip(cy, cx, radii):
  circy, circx = circle_perimeter(center_y, center_x, radius,shape=img_rgb.shape)
  img_rgb[circy, circx] = (220, 20, 20)


# Plot images
imgs = [img_rgb, edges]
r,c = 1,2
fig, axs = plt.subplots(r, c, figsize=(15,15))
for i in range(r):
    for j in range(c):
        axs[j].imshow(imgs[i*c+j], cmap='gray')
        axs[j].axis('off')

这是head_CT.tif图像。

谢谢你的帮助。

*此图像来自 Gonzalez & Woods，数字图像处理一书。

标签： pythonopencvcomputer-visionscikit-imagetomography-reconstruction