Question

我想基于现有细分创建骨架，类似于此处所做的（来自sk-image）：

但我想在3D数据上执行此操作。那里有代码吗？最好是在python中，但任何语言都有帮助。

我知道this很棒的网站，但我认为他们不会提供任何代码。

我计划在大约500x500x500像素的卷上使用它，所以它应该可以很好地扩展......

Answer 1

我正在以下链接中开发此工具。名称 convOptimize.py 程序中的 getSkeletonize3D 功能可让您细化3D数据。我花了大约30分钟给出了512立方体的结果。如果您有任何问题，请告诉我。 https://github.com/3Scan/3scan-skeleton。我用于实现的论文在下面的代码中的注释中

基本上这个3D骨架化算法是如何工作的，在每次传递中它有12个子项，在这些子项中迭代地去除特定方向上的边界，直到你在中心得到一个骨架。

骨架化数据所需的主要python代码如下所示。因为它需要从不同的其他porgrams中导入rotateOperators，它从另一个名为Thin3dtemplates的文件导入。我建议你downlaod rotateOperators，Thin3dtemplates，convoptimize python脚本文件，还下载lookuparray.npy这是一个文件，用作numpy数组格式的查找表，预先计算用于验证体素是否标记为要删除。你需要python＆gt;安装了3个版本，scipy，numpy和pyeda模块来运行这些代码。

import numpy as np
import time
from scipy import ndimage
from scipy.ndimage.filters import convolve

"""
   the following subiteration functions are how each image is rotated to the next direction for removing
   boundary voxels in the order described in the reference paper
   us, ne, wd,..
"""
from rotationalOperators import firstSubiteration, secondSubiteration, thirdSubiteration, fourthSubiteration, fifthSubiteration, sixthSubiteration, seventhSubiteration, eighthSubiteration, ninthSubiteration, tenthSubiteration, eleventhSubiteration, twelvethSubiteration

"""
   reference paper
   http://web.inf.u-szeged.hu/ipcg/publications/papers/PalagyiKuba_GMIP1999.pdf
   input should be a binary image/ already segmented
"""


"""
   array that has calculated the validity of the 14 templates beforehand and stored each index which is
   decimal number of the binary string of 26 values (sqrt(3) connectivity) that are around a single voxel 
"""

lookUpTablearray = np.load('lookupTablearray.npy')


def _convolveImage(arr, flippedKernel):
    arr = np.ascontiguousarray(arr, dtype=np.uint64)
    result = convolve(arr, flippedKernel, mode='constant', cval=0)
    result[arr == 0] = 0
    return result


"""
each of the 12 iterations corresponds to each of the following
directions - us, ne, wd, es, uw, nd, sw, un, ed, nw, ue, sd
imported from template expressions
evaluated in advance using pyeda
https://pyeda.readthedocs.org/en/latest/expr.html
"""

sElement = ndimage.generate_binary_structure(3, 1)


def _getBouondariesOfimage(image):
    """
       function to find boundaries/border/edges of the array/image
    """

    erode_im = ndimage.morphology.binary_erosion(image, sElement)
    boundaryIm = image - erode_im
    return boundaryIm

"""
each of the 12 iterations corresponds to each of the following
directions - us, ne, wd, es, uw, nd, sw, un, ed, nw, ue, sd
imported from template expressions
evaluated in advance using pyeda
https://pyeda.readthedocs.org/en/latest/expr.html
"""

directionList = [firstSubiteration, secondSubiteration, thirdSubiteration, fourthSubiteration,
                 fifthSubiteration, sixthSubiteration, seventhSubiteration, eighthSubiteration,
                 ninthSubiteration, tenthSubiteration, eleventhSubiteration, twelvethSubiteration]


def _skeletonPass(image):
    """
        each pass consists of 12 serial subiterations and finding the
        boundaries of the padded image/array
    """
    boundaryIm = _getBouondariesOfimage(image)
    numPixelsremovedList = [] * 12
    boundaryIndices = list(set(map(tuple, list(np.transpose(np.nonzero(boundaryIm))))))
    for i in range(0, 12):
        convImage = _convolveImage(image, directionList[i])
        totalPixels, image = _applySubiter(image, boundaryIndices, convImage)
        print("number of pixels removed in the {} direction is {}". format(i, totalPixels))
        numPixelsremovedList.append(totalPixels)
    numPixelsremoved = sum(numPixelsremovedList)
    return numPixelsremoved, image


def _applySubiter(image, boundaryIndices, convImage):
    """
       each subiteration paralleley reduces the border voxels in 12 directions
       going through each voxel and marking if it can be deleted or not in a
       different image named temp_del and finally multiply it with the original
       image to delete the voxels so marked
    """
    temp_del = np.zeros_like(image)
    # boundaryIndicesCopy = copy.deepcopy(boundaryIndices)
    lenB = len(boundaryIndices)
    for k in range(0, lenB):
        temp_del[boundaryIndices[k]] = lookUpTablearray[convImage[boundaryIndices[k]]]
    numpixel_removed = np.einsum('ijk->', image * temp_del, dtype=int)
    image[temp_del == 1] = 0
    return numpixel_removed, image


def getSkeletonize3D(image):
    """
    function to skeletonize a 3D binary image with object in brighter contrast than background.
    In other words, 1 = object, 0 = background
    """
    assert np.max(image) in [0, 1]
    zOrig, yOrig, xOrig = np.shape(image)
    padImage = np.lib.pad(image, 1, 'constant', constant_values=0)
    start_skeleton = time.time()
    pass_no = 0
    numpixel_removed = 0
    while pass_no == 0 or numpixel_removed > 0:
        numpixel_removed, padImage = _skeletonPass(padImage)
        print("number of pixels removed in pass {} is {}".format(pass_no, numpixel_removed))
        pass_no += 1
    print("done %i number of pixels in %f seconds" % (np.sum(image), time.time() - start_skeleton))
    return padImage[1: zOrig + 1, 1: yOrig + 1, 1: xOrig + 1]

if __name__ == '__main__':
    sample = np.ones((5, 5, 5), dtype=np.uint8)
    resultSkel = getSkeletonize3D(sample)
    # gives a single voxel at the center
    print("resultSkel", resultSkel)

从分割的3d骨骼

1 个答案: