Python+OpenCV实现图像医学处理

在Jupyter Notebook上使用Python+opencv实现如下图像医学处理。关于opencv库的安装可以参考：Python下opencv库的安装过程与一些问题汇总。

 

1.实现代码

import cv2
from skimage import morphology, io
import matplotlib.pyplot as plt

#读取原图片
img1=cv2.imread("vas0.bmp")
cv2.imshow("img1", img1)

#阈值分割
img2=cv2.adaptiveThreshold(cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY),255,cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY,15,3)#自适应阈值分割
cv2.imshow('img2', img2)

#图像反色
h,w = img2.shape
img3=img2.copy()
for i in range(h):
    for j in range(w):
        img3[i,j] = 255-img3[i,j]
cv2.imshow('img3', img3)

#图像扩展
img4 = cv2.copyMakeBorder(img3,1,1,1,1,cv2.BORDER_REFLECT)
cv2.imshow('img4', img4)

#去除小面积区域
contours,hierarch=cv2.findContours(img4,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)#轮廓提取
for i in range(len(contours)):
    area = cv2.contourArea(contours[i]) #计算轮廓所占面积
    if area < 200:                         #将area小于阈值区域填充背景色，OpenCV读出的是BGR值
        cv2.drawContours(img4,[contours[i]],-1, (0,0,0), thickness=-1)     #原始图片背景BGR值为(0,0,0)
cv2.imshow('img5',img4)

#进一步细化，去除较小面积区域，保留最大连通区域
img5=img4.copy()
contours1,hierarchy = cv2.findContours(img5, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
for i in range(len(contours1)):
    area1 = cv2.contourArea(contours1[i])
    print(area1)
    if area1 ==214.5:
        cv2.drawContours(img5,[contours1[i]],-1, (0,0,0), thickness=-1)
    elif area1==363.0:
        cv2.drawContours(img5,[contours1[i]],-1, (0,0,0), thickness=-1)
cv2.imshow('img6',img5)

#实施骨架算法
img6=img5.copy()
cv2.threshold(img6,80,1,0,img6)
img7 =morphology.skeletonize(img6)
plt.imshow(img7,cmap=plt.cm.gray)
plt.axis('off')
plt.show()

#图像反色
h1,w1 = img7.shape
img8=img7.copy()
for i in range(h1):
    for j in range(w1):
        img8[i,j] = 1-img8[i,j]
plt.imshow(img8,cmap=plt.cm.gray)
plt.axis('off')
plt.show()

#边缘提取
img9 = cv2.Canny(img5,80,255)
cv2.imshow("img9",img9)

#图像反色
h2,w2 = img9.shape
img10=img9.copy()
for i in range(h2):
    for j in range(w2):
        img10[i,j] = 255-img10[i,j]
cv2.imshow("img10",img10)
cv2.waitKey(0)

 

2.运行结果