利用openslide-python处理病理

 参考：

博客总结https://www.jianshu.com/p/bd5b572b5269

官方文档：https://openslide.org/api/python/#module-openslide

• 获取元信息，如每个像素有多少微米

import openslide

wsi = openslide.Openslide(wsi_file)
mpp_x = wsi_ob.properties[openslide.PROPERTY_NAME_MPP_X]
mpp_y = wsi_ob.properties[openslide.PROPERTY_NAME_MPP_Y]

In [8]: from openslide import OpenSlide

# 读入一个WSI图像，生成一个slide实例
In [9]: slide = OpenSlide('tumor_001.tif')

# 原始图像经各个level水平缩放后的长和宽
In [10]: slide.level_dimensions
Out[10]:
((97792, 221184),
 (48896, 110592),
 (24448, 55296),
 (12224, 27648),
 (6112, 13824),
 (3056, 6912),
 (1528, 3456),
 (764, 1728),
 (382, 864),
 (191, 432))

# 在医学影像处理中，常见的是将图像进行level8水平的缩放
In [11]: slide.level_dimensions[8]
Out[11]: (382, 864)

# downsamples,网上都成为下采样因子，可能不好理解，它代表的将原始WSI图像进行缩放的倍数，与level_dimentions是相对应的
In [12]: slide.level_downsamples
Out[12]: (1.0, 2.0, 4.0, 8.0, 16.0, 32.0, 64.0, 128.0, 256.0, 512.0)

# 将level_dimentions图像的长宽乘以对应的level_downsamples的缩放倍数，就可以得到原始图像大小
In [16]: np.array(slide.level_dimensions[-1])*slide.level_downsamples[-1]
Out[16]: array([ 97792., 221184.])

# 查看WSI的level数
In [17]: slide.level_count
Out[17]: 10

# read_region(location, level, size)，Return an RGBA Image containing the contents of the specified region.
# Parameters:  
# location (tuple) tuple giving the top left pixel in the level 0 reference frame
# level (int)  the level number
# size (tuple) (width, height) tuple giving the region size
# read_region()返回一个数组的rgba数值数组
In [21]: slide.read_region((0,0),8,slide.level_dimensions[8])
Out[21]: 

In [23]: np.array(img)
Out[23]:
array([[[255, 255, 255, 255],
        [255, 255, 255, 255],
        [251, 251, 251, 255],
        ...,
        [255, 255, 255, 255],
        [254, 255, 253, 255],
        [252, 254, 251, 255]],
        ... 
      [[250, 250, 250, 255],
        [255, 255, 255, 255],
        [251, 251, 251, 255],
        ...,
        [252, 252, 252, 255],
        [250, 250, 250, 255],
        [251, 251, 251, 255]]], dtype=uint8)

In [24]: rgb_arr = np.array(img)

# 查看一下图像rgb数组信息
In [26]: rgb_arr.size
Out[26]: 1320192

In [27]: rgb_arr.shape
Out[27]: (864, 382, 4)

In [28]: rgb_arr.ndim
Out[28]: 3

In [29]: rgb_arr[0]
Out[29]:
array([[255, 255, 255, 255],
       [255, 255, 255, 255],
       [251, 251, 251, 255],
       ...,
       [255, 255, 255, 255],
       [254, 255, 253, 255],
       [252, 254, 251, 255]], dtype=uint8)

In [30]: rgb_arr[0].shape
Out[30]: (382, 4)