import cv2
import numpy
import dicom
from matplotlib import pyplot as plt

# 读取单张Dicom图像
dcm = dicom.read_file("../Data/vhm.420.dcm")
dcm.image = dcm.pixel_array * dcm.RescaleSlope + dcm.RescaleIntercept

# 获取图像中的像素数据
slices = []
slices.append(dcm)

# 复制Dicom图像中的像素数据
img = slices[ int(len(slices)/2) ].image.copy()

# 对图像进行阈值分割
ret,img = cv2.threshold(img, 90,3071, cv2.THRESH_BINARY)
img = numpy.uint8(img)

# 提取分割结果中的轮廓，并填充孔洞
im2, contours, _ = cv2.findContours(img,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)
mask = numpy.zeros(img.shape, numpy.uint8)
for contour in contours:
    cv2.fillPoly(mask, [contour], 255)
img[(mask > 0)] = 255

# 对分割结果进行形态学的开操作
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(2,2))
img = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel)

# 根据分割mask获取分割结果的像素数据
img2 = slices[ int(len(slices)/2) ].image.copy()
img2[(img == 0)] = -2000

# 显式原始数据，mask和分割结果
plt.figure(figsize=(12, 12))
plt.subplot(131)
plt.imshow(slices[int(len(slices) / 2)].image, ‘gray‘)
plt.title(‘Original‘)
plt.subplot(132)
plt.imshow(img, ‘gray‘)
plt.title(‘Mask‘)
plt.subplot(133)
plt.imshow(img2, ‘gray‘)
plt.title(‘Result‘)
plt.show()