函数"im2uint8"之间的区别在于:(在MATLAB中)和"bytescale"(在Python中) [英] Difference between the functions "im2uint8" (in MATLAB) and "bytescale" (in Python)

问题描述

我想将DICOM图像从int16转换为uint8.我已经在Python中使用 Z_axis = bytescale(img)完成了此操作，但这与在MATLAB中使用 im2uint8 产生了不同的结果.在MATLAB中，使用 im2uint8 转换为uint8后，DICOM图像的最小值和最大值分别为(124，136).但是在使用 bytescale 进行转换后，Python中的这些值为(0，255).

I want to convert a DICOM image from int16 to uint8. I have done it in Python using Z_axis = bytescale(img), but this gives different results than using im2uint8 in MATLAB. In MATLAB, The minimum and maximum values of a DICOM image after converting to uint8 using im2uint8 are (124, 136), respectively. But these values in Python after converting using bytescale are (0, 255).

Python代码:

for person in range(0, len(dirs1)):
if not os.path.exists(os.path.join(directory, dirs1[person])):
    Pathnew = os.path.join(directory, dirs1[person])
    os.makedirs(Pathnew)
    for root, dirs, files in os.walk(os.path.join(path, dirs1[person])):
        dcmfiles = [_ for _ in files if _.endswith('.dcm')]
        for dcmfile in dcmfiles:
            dcm_image = pydicom.read_file(os.path.join(root, dcmfile))
            img = dcm_image.pixel_array
            Z_axis = bytescale(img)  
            minVal = Z_axis.min()
            maxVal = Z_axis.max()

Matlab代码:

for j = 1 : length(Files2)
    img = dicomread([galleryPath Files2(j).name]);
    Z_axis = im2uint8(img);
    minVal = min(min(Z_axis));
    maxVal = max(max(Z_axis));

图像在显示时看起来是相等的，但数值却不相等.那么， bytescale 和 im2uint8 函数是否相等?如果没有，我想要Python中的 im2uint8 之类的结果.我应该选择哪种功能(特别是对于DICOM图像)?

The images look equal when displayed, but the numeric values are not. So, are the bytescale and im2uint8 functions equal or not? If not, I want results like im2uint8 in Python. What kind of function should I choose (especially for DICOM images)?

例如，在MATLAB中读取DICOM文件后:

For example, in MATLAB after reading a DICOM file:

img = dicomread([galleryPath Files2(j).name]);
img = [ -1024,   -1024,   16;
        -1024,       8,   11;
           17,       5,    8];

但是在Python中，读取后的图像是:

But in Python, the same image after reading is:

dcm_image = pydicom.read_file(os.path.join(root, dcmfile))
img = dcm_image.pixel_array
img = array([[ -1024,    -1024,   27],
             [ -1024,       27,   26],
             [    24,       26,   23]])

我不知道为什么它们在MATLAB和Python中与众不同.在MATLAB中应用 im2uint8 后，输出为:

I don't know why they are different in MATLAB and Python. After applying im2uint8 in MATLAB, the output is:

Z_axis = im2uint8(img)
Z_axis =
 3×3 uint8 matrix
   124    124   128
   124    128   128
   128    128   128

然后在Python中应用 bytescale 后，输出为:

And after applying bytescale in Python, the output is:

bytescale(img)
Z_axis = 
    array([[0,    0,   83],
           [0,   83,   83],
           [83,  83,   83]], dtype=uint8)

推荐答案

首先，关于读取数据的问题，我建议使用

Firstly, regarding the issue with reading the data, I would suggest using dcmread in Python, as that gave me the same exact data as dicomread in MATLAB.

其次，在MATLAB中，当 im2uint8 转换 int16 的值，它将假定分别等于-32768和32767的数据的最小值和最大值(即，由 int16 表示的最小值和最大值)来缩放它们).对于 bytescale > 具有相同的行为，我相信您需要相应地设置 cmin 和 cmax 参数(因为否则它们将默认为 data.min()和 data.max()).这应该在Python中复制 im2uint8 的结果:

Secondly, in MATLAB when im2uint8 converts int16 values it will scale them assuming minimum and maximum values for the data equal to -32768 and 32767, respectively (i.e. the minimum and maximum values representable by an int16). For bytescale to behave equivalently, I believe you need to set the cmin and cmax arguments accordingly (since they will otherwise default to data.min() and data.max(), respectively). This should replicate the results of im2uint8 in Python:

Z_axis = bytescale(img.astype(float), cmin=-32768, cmax=32767)

注意:必须先将数据转换为浮点数，以解决 bytescale 中的明显错误，该错误无法正确处理整数算术(由

Note: conversion of the data to float first is necessary to account for an apparent bug in bytescale that doesn't handle the integer arithmetic properly (found courtesy of Cris Luengo).

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