Matlab:如何在图像中弯曲直线

Question

我手上有一张图片(png格式)。连接椭圆的线(代表原子核)是超过直线的，这是不切实际的。我如何从图像中提取出这些线，使它们弯曲，前提是它们仍然包围着细胞核。

下图如下：

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弯曲后

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编辑：如何将answer2中的扩展和部分转换为Matlab语言？我搞不懂。

Answer 1

好的，这里有一种方法，需要几个随机化步骤才能得到“自然”的非对称外观。

我把实际的代码发布在Mathematica中，以防有人关心把它翻译到Matlab中。

(* A preparatory step: get your image and clean it*)
i = Import@"http://i.stack.imgur.com/YENhB.png";
i1 = Image@Replace[ImageData[i], {0., 0., 0.} -> {1, 1, 1}, {2}];
i2 = ImageSubtract[i1, i];
i3 = Inpaint[i, i2]

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(*Now reduce to a skeleton to get a somewhat random starting point.  
The actual algorithm for this dilation does not matter, as far as we 
get a random area slightly larger than the original elipses *)
id = Dilation[SkeletonTransform[
             Dilation[SkeletonTransform@ColorNegate@Binarize@i3, 3]], 1] 

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(*Now the real random dilation loop*)
(*Init vars*)
p = Array[1 &, 70]; j = 1;
(*Store in w an image with a different color for each cluster, so we 
can find edges between them*)
w = (w1 = 
      WatershedComponents[
       GradientFilter[Binarize[id, .1], 1]]) /. {4 -> 0} // Colorize;
(*and loop ...*)
For[i = 1, i < 70, i++,
 (*Select edges in w and dilate them with a random 3x3 kernel*)
 ed = Dilation[EdgeDetect[w, 1], RandomInteger[{0, 1}, {3, 3}]];
 (*The following is the core*)
 p[[j++]] = w =
   ImageFilter[  (* We apply a filter to the edges*)
    (Switch[
          Length[#1],  (*Count the colors in a 3x3 neighborhood of each pixel*)
          0, {{{0, 0, 0}, 0}},          (*If no colors, return bkg*)
          1, #1,                        (*If one color, return it*)
          _, {{{0, 0, 0}, 0}}])[[1, 1]] (*If more than one color, return bkg*)&@
      Cases[Tally[Flatten[#1, 1]], 
       Except[{{0.`, 0.`, 0.`}, _}]] & (*But Don't count bkg pixels*),
    w, 1,
    Masking -> ed,       (*apply only to edges*)
    Interleaving -> True (*apply to all color chanels at once*)]
 ]

结果是：

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编辑

对于面向Mathematica的读取器来说，最后一个循环的函数代码可能更容易(而且更短)：

NestList[
 ImageFilter[  
   If[Length[#1] ==  1, #1[[1, 1]], {0, 0, 0}] &@
     Cases[Tally[Flatten[#1, 1]], Except[{0.` {1, 1, 1}, _}]] & , #, 1,
   Masking      -> Dilation[EdgeDetect[#, 1], RandomInteger[{0, 1}, {3, 3}]],  
   Interleaving -> True ] &,
 WatershedComponents@GradientFilter[Binarize[id,.1],1]/.{4-> 0}//Colorize, 
5]

Answer 2

您输入的是Voronoi图。您可以使用另一个距离函数而不是欧几里得函数重新计算它。

下面是数学中使用曼哈顿距离的一个例子(i3是您输入的没有线条的图像)：

ColorCombine[{Image[
   WatershedComponents[
    DistanceTransform[Binarize@i3, 
      DistanceFunction -> ManhattanDistance] ]], i3, i3}]

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编辑

我正在使用另一种算法(初步结果)。你认为如何？

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Answer 3

这是我想出来的，它不是@belisarius代码的直接翻译，但应该足够接近。

%# read image (indexed image)
[I,map] = imread('http://i.stack.imgur.com/YENhB.png');

%# extract the blobs (binary image)
BW = (I==1);

%# skeletonization + dilation
BW = bwmorph(BW, 'skel', Inf);
BW = imdilate(BW, strel('square',2*1+1));

%# connected components
L = bwlabel(BW);
imshow(label2rgb(L))

%# filter 15x15 neighborhood
for i=1:13
    L = nlfilter(L, [15 15], @myFilterFunc);
    imshow( label2rgb(L) )
end

%# result
L(I==1) = 0;                %# put blobs back
L(edge(L,'canny')) = 0;     %# edges
imshow( label2rgb(L,@jet,[0 0 0]) )

myFilterFunc.m

function p = myFilterFunc(x)
    if range(x(:)) == 0
        p = x(1);                %# if one color, return it
    else
        p = mode(x(x~=0));       %# else, return the most frequent color
    end
end

结果：

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下面是这个过程的动画：

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