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在尝试将opencv与dicom单色文件一起使用时，我只看到了一个解决方案：首先将单色dicom文件转换为RGB中像素值在-2000（黑色）到2000（白色）之间的像素值，0<=R=G=B<= 255. （为了确保灰度，我必须设置 R=G=B）所以我做了一个线性插值，从第一个 [-2000;2000] 到 [0, 255]。我的照片的结果并不好，所以我决定在下面放置一个所有像素都是黑色的黑色三边形和一个上面所有像素都是白色的白色三边形。这样做，我可以使用 opencv，但是 1）我想自动化黑色阈值和白色三阈值 2）因为我有 512*512 像素，所以双循环需要时间来执行。

你知道我如何自动化和加速这个过程吗？或者只是一个好主意？代码是：

    # import the necessary packages
from imutils import contours
import matplotlib.pyplot as plt
import matplotlib.mlab as mlab
import scipy
from skimage import measure
import numpy as np # numeric library needed
import pandas as pd #for dataframe
import argparse # simple argparser
import imutils
import cv2  # for opencv image recognising tool
import dicom
from tkinter import Tk
from tkinter.filedialog import askopenfilename
import pdb

#filename = askopenfilename() # show an "Open" dialog box and return the path to the selected file
#filename ="../inputs/12e0e2036f61c8a52ee4471bf813c36a/7e74cdbac4c6db70bade75225258119d.dcm"
dicom_file = dicom.read_file(filename) ## original dicom File
#### a dicom monochrome file has pixel value between approx -2000 and +2000, opencv doesn't work with it#####
#### in a first step we transform those pixel values in (R,G,B)
### to have gray in RGB, simply give the same values for R,G, and B, 
####(0,0,0) will be black, (255,255,255) will be white,

## the threeshold to be automized with a proper quartile function of the pixel distribution
black_threeshold=0###pixel value below 0 will be black,
white_threeshold=1400###pixel value above 1400 will be white
wt=white_threeshold
bt=black_threeshold

###### function to transform a dicom to RGB for the use of opencv, 
##to be strongly improved, as it takes to much time to run,
## and the linear process should be replaced with an adapted weighted arctan function.
def DicomtoRGB(dicomfile,bt,wt):
    """Create new image(numpy array) filled with certain color in RGB"""
    # Create black blank image
    image = np.zeros((dicomfile.Rows, dicomfile.Columns, 3), np.uint8)
    #loops on image height and width
    i=0
    j=0
    while i<dicomfile.Rows:
        j=0
        while j<dicomfile.Columns:
            color = yaxpb(dicom_file.pixel_array[i][j],bt,wt) #linear transformation to be adapted
            image[i][j] = (color,color,color)## same R,G, B value to obtain greyscale
            j=j+1
        i=i+1
    return image
##linear transformation : from [bt < pxvalue < wt] linear to [0<pyvalue<255]: loss of information... 
def yaxpb(pxvalue,bt,wt):
    if pxvalue < bt:
        y=0
    elif pxvalue > wt:
        y=255
    else:
        y=pxvalue*255/(wt-bt)-255*bt/(wt-bt)
    return y



image=DicomtoRGB(dicom_file,bt=0,wt=1400)
>>image
array([[[0, 0, 0],
        [0, 0, 0],
        [0, 0, 0],
        ..., 
        [0, 0, 0],
        [0, 0, 0],
        [0, 0, 0]],

       [[0, 0, 0],
        [0, 0, 0],
        [0, 0, 0],
        ..., 
        [0, 0, 0],
        [0, 0, 0],
        [0, 0, 0]],

       [[0, 0, 0],
        [0, 0, 0],
        [0, 0, 0],
        ..., 
        [0, 0, 0],
        [0, 0, 0],
        [0, 0, 0]],

       ..., 
       [[0, 0, 0],
        [0, 0, 0],
        [0, 0, 0],
        ..., 
        [0, 0, 0],
        [0, 0, 0],
        [0, 0, 0]],

       [[0, 0, 0],
        [0, 0, 0],
        [0, 0, 0],
        ..., 
        [0, 0, 0],
        [0, 0, 0],
        [0, 0, 0]],

       [[0, 0, 0],
        [0, 0, 0],
        [0, 0, 0],
        ..., 
        [0, 0, 0],
        [0, 0, 0],
        [0, 0, 0]]], dtype=uint8)

## loading the RGB in a proper opencv format
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
## look at the gray file
cv2.imshow("gray", gray)
cv2.waitKey(0)
cv2.destroyWindow("gray")
4

2 回答 2

0

我认为您的问题与此有关：

...双 for 循环需要时间来执行。

您可以使用opencv中的remap函数：请参阅此示例：

于 2017-03-31T12:26:06.533 回答
0

EDIT2 - 现在执行正确的转换

我们可以使用 . 矢量化您的整个代码numpy。这是一个例子： 

import numpy as np

def dicom_to_rgb(img,bt,wt):

    # enforce boundary conditions
    img = np.clip(img,bt,wt)

    # linear transformation
    # multiplicative
    img = np.multiply(img,-255/(wt-bt)).astype(np.int)
    # additive
    img += 255

    # stack thrice on the last axis for R G B
    rgb_img = np.stack([img]*3,axis=-1)

    return rgb_img


    pixels = 512
    img = np.random.randint(-2000,2000,pixels**2).reshape(pixels,pixels)
    bt = 0
    wt = 1400

    rgb = dicom_to_rgb(img,bt,wt)

或根据您的输入：

dicom_file = dicom.read_file(filename)
img = np.array(dicom_file.pixel_array)

rgb = dicom_to_rgb(img,wt,bt)
于 2017-03-31T11:47:27.400 回答