Python+OpenCV實現圖像醫學處理

在Jupyter Notebook上使用Python+opencv實現如下圖像醫學處理。關於opencv庫的安裝可以參考：Python下opencv庫的安裝過程與一些問題匯總。

 

1.實現代碼

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

#讀取原圖片
img1=cv2.imread("vas0.bmp")
cv2.imshow("img1", img1)

#閾值分割
img2=cv2.adaptiveThreshold(cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY),255,cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY,15,3)#自適應閾值分割
cv2.imshow('img2', img2)

#圖像反色
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)

#圖像擴展
img4 = cv2.copyMakeBorder(img3,1,1,1,1,cv2.BORDER_REFLECT)
cv2.imshow('img4', img4)

#去除小面積區域
contours,hierarch=cv2.findContours(img4,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)#輪廓提取
for i in range(len(contours)):
    area = cv2.contourArea(contours[i]) #計算輪廓所占面積
    if area < 200:                         #將area小於閾值區域填充背景色，OpenCV讀出的是BGR值
        cv2.drawContours(img4,[contours[i]],-1, (0,0,0), thickness=-1)     #原始圖片背景BGR值為(0,0,0)
cv2.imshow('img5',img4)

#進一步細化，去除較小面積區域，保留最大連通區域
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)

#實施骨架算法
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()

#圖像反色
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()

#邊緣提取
img9 = cv2.Canny(img5,80,255)
cv2.imshow("img9",img9)

#圖像反色
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.運行結果