Branching points detection in processed image












6












$begingroup$


I want to get the branching coordinates from a computer-generated image like this one:



Generated tree



After SkeletonTransform and Pruning, I get:



Skeletonized



I would like to develop an algorithm that automatically detect and give coordinates of branching points (I could do it manually but really time consuming).



Thanks!



Edit: I already tried MorphologicalBranchPoints with poor results...






graphics graphs-and-networks image-processing







share|improve this question











$endgroup$








  • 1




    $begingroup$
    "I already tried MorphologicalBranchPoints with poor results..." Can you be specific about what you got and what you want instead?
    $endgroup$
    – Szabolcs
    Feb 18 at 14:48










  • $begingroup$
    @ Szabolcs It was more about a careful selection of thinning and skeletonize options. The result with MorphologicalBranchPoints wasn't good then.
    $endgroup$
    – Valacar
    Feb 18 at 15:08






  • 1




    $begingroup$
    Added another update.
    $endgroup$
    – Szabolcs
    Feb 18 at 15:09
















6












$begingroup$


I want to get the branching coordinates from a computer-generated image like this one:



Generated tree



After SkeletonTransform and Pruning, I get:



Skeletonized



I would like to develop an algorithm that automatically detect and give coordinates of branching points (I could do it manually but really time consuming).



Thanks!



Edit: I already tried MorphologicalBranchPoints with poor results...






graphics graphs-and-networks image-processing







share|improve this question











$endgroup$








  • 1




    $begingroup$
    "I already tried MorphologicalBranchPoints with poor results..." Can you be specific about what you got and what you want instead?
    $endgroup$
    – Szabolcs
    Feb 18 at 14:48










  • $begingroup$
    @ Szabolcs It was more about a careful selection of thinning and skeletonize options. The result with MorphologicalBranchPoints wasn't good then.
    $endgroup$
    – Valacar
    Feb 18 at 15:08






  • 1




    $begingroup$
    Added another update.
    $endgroup$
    – Szabolcs
    Feb 18 at 15:09














6












6








6


1



$begingroup$


I want to get the branching coordinates from a computer-generated image like this one:



Generated tree



After SkeletonTransform and Pruning, I get:



Skeletonized



I would like to develop an algorithm that automatically detect and give coordinates of branching points (I could do it manually but really time consuming).



Thanks!



Edit: I already tried MorphologicalBranchPoints with poor results...






graphics graphs-and-networks image-processing







share|improve this question











$endgroup$




I want to get the branching coordinates from a computer-generated image like this one:



Generated tree



After SkeletonTransform and Pruning, I get:



Skeletonized



I would like to develop an algorithm that automatically detect and give coordinates of branching points (I could do it manually but really time consuming).



Thanks!



Edit: I already tried MorphologicalBranchPoints with poor results...






graphics graphs-and-networks image-processing




graphics graphs-and-networks image-processing






share|improve this question















share|improve this question













share|improve this question




share|improve this question








edited Feb 18 at 14:42







Valacar

















asked Feb 18 at 14:35









ValacarValacar

513213




513213








  • 1




    $begingroup$
    "I already tried MorphologicalBranchPoints with poor results..." Can you be specific about what you got and what you want instead?
    $endgroup$
    – Szabolcs
    Feb 18 at 14:48










  • $begingroup$
    @ Szabolcs It was more about a careful selection of thinning and skeletonize options. The result with MorphologicalBranchPoints wasn't good then.
    $endgroup$
    – Valacar
    Feb 18 at 15:08






  • 1




    $begingroup$
    Added another update.
    $endgroup$
    – Szabolcs
    Feb 18 at 15:09














  • 1




    $begingroup$
    "I already tried MorphologicalBranchPoints with poor results..." Can you be specific about what you got and what you want instead?
    $endgroup$
    – Szabolcs
    Feb 18 at 14:48










  • $begingroup$
    @ Szabolcs It was more about a careful selection of thinning and skeletonize options. The result with MorphologicalBranchPoints wasn't good then.
    $endgroup$
    – Valacar
    Feb 18 at 15:08






  • 1




    $begingroup$
    Added another update.
    $endgroup$
    – Szabolcs
    Feb 18 at 15:09








1




1




$begingroup$
"I already tried MorphologicalBranchPoints with poor results..." Can you be specific about what you got and what you want instead?
$endgroup$
– Szabolcs
Feb 18 at 14:48




$begingroup$
"I already tried MorphologicalBranchPoints with poor results..." Can you be specific about what you got and what you want instead?
$endgroup$
– Szabolcs
Feb 18 at 14:48












$begingroup$
@ Szabolcs It was more about a careful selection of thinning and skeletonize options. The result with MorphologicalBranchPoints wasn't good then.
$endgroup$
– Valacar
Feb 18 at 15:08




$begingroup$
@ Szabolcs It was more about a careful selection of thinning and skeletonize options. The result with MorphologicalBranchPoints wasn't good then.
$endgroup$
– Valacar
Feb 18 at 15:08




1




1




$begingroup$
Added another update.
$endgroup$
– Szabolcs
Feb 18 at 15:09




$begingroup$
Added another update.
$endgroup$
– Szabolcs
Feb 18 at 15:09










2 Answers
2






active

oldest

votes


















11












$begingroup$

Use MorphologicalBranchPoints.



im = Binarize@Import["https://i.stack.imgur.com/O0AMj.png"]



skel = Pruning[Thinning[im], 20];



HighlightImage[skel, MorphologicalBranchPoints[skel]]


enter image description here



Another possibility is to use



skel1 = Pruning[Thinning[im, Method -> "MedialAxis"], 10];


as a start then smoothen the result using 



skel2 = Thinning@Dilation[skel1, 5]


so that MorphologicalBranchPoints would not give false results.



HighlightImage[skel2, MorphologicalBranchPoints[skel2]]


enter image description here






share|improve this answer











$endgroup$













  • $begingroup$
    Great ! Thanks a lot.
    $endgroup$
    – Valacar
    Feb 18 at 15:09



















7












$begingroup$

I notice there's a faint pink background that seems like a natural boundary. I've highlighted it to showcase this:



enter image description here



We can extract this curve and use it as the original boundary:



im = Import["https://i.stack.imgur.com/7Ck2S.png"];

mask = FillingTransform[Thinning[Binarize[ColorReplace[im, White -> Black, .055], 0]], CornerNeighbors -> True]


enter image description here



And the simply call MorphologicalBranchPoints:



skel = Thinning[mask];

HighlightImage[skel, MorphologicalBranchPoints[skel], 1]


enter image description here






share|improve this answer











$endgroup$













  • $begingroup$
    This is really clean!
    $endgroup$
    – Valacar
    Feb 19 at 9:24










  • $begingroup$
    by the way I think it's Thinning[mask] in this case, isn't it?
    $endgroup$
    – Valacar
    Feb 19 at 12:26










  • $begingroup$
    @Valacar Yes. I made the correction, thank you.
    $endgroup$
    – Chip Hurst
    Feb 19 at 12:36













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2 Answers
2






active

oldest

votes








2 Answers
2






active

oldest

votes









active

oldest

votes






active

oldest

votes









11












$begingroup$

Use MorphologicalBranchPoints.



im = Binarize@Import["https://i.stack.imgur.com/O0AMj.png"]



skel = Pruning[Thinning[im], 20];



HighlightImage[skel, MorphologicalBranchPoints[skel]]


enter image description here



Another possibility is to use



skel1 = Pruning[Thinning[im, Method -> "MedialAxis"], 10];


as a start then smoothen the result using 



skel2 = Thinning@Dilation[skel1, 5]


so that MorphologicalBranchPoints would not give false results.



HighlightImage[skel2, MorphologicalBranchPoints[skel2]]


enter image description here






share|improve this answer











$endgroup$













  • $begingroup$
    Great ! Thanks a lot.
    $endgroup$
    – Valacar
    Feb 18 at 15:09
















11












$begingroup$

Use MorphologicalBranchPoints.



im = Binarize@Import["https://i.stack.imgur.com/O0AMj.png"]



skel = Pruning[Thinning[im], 20];



HighlightImage[skel, MorphologicalBranchPoints[skel]]


enter image description here



Another possibility is to use



skel1 = Pruning[Thinning[im, Method -> "MedialAxis"], 10];


as a start then smoothen the result using 



skel2 = Thinning@Dilation[skel1, 5]


so that MorphologicalBranchPoints would not give false results.



HighlightImage[skel2, MorphologicalBranchPoints[skel2]]


enter image description here






share|improve this answer











$endgroup$













  • $begingroup$
    Great ! Thanks a lot.
    $endgroup$
    – Valacar
    Feb 18 at 15:09














11












11








11





$begingroup$

Use MorphologicalBranchPoints.



im = Binarize@Import["https://i.stack.imgur.com/O0AMj.png"]



skel = Pruning[Thinning[im], 20];



HighlightImage[skel, MorphologicalBranchPoints[skel]]


enter image description here



Another possibility is to use



skel1 = Pruning[Thinning[im, Method -> "MedialAxis"], 10];


as a start then smoothen the result using 



skel2 = Thinning@Dilation[skel1, 5]


so that MorphologicalBranchPoints would not give false results.



HighlightImage[skel2, MorphologicalBranchPoints[skel2]]


enter image description here






share|improve this answer











$endgroup$



Use MorphologicalBranchPoints.



im = Binarize@Import["https://i.stack.imgur.com/O0AMj.png"]



skel = Pruning[Thinning[im], 20];



HighlightImage[skel, MorphologicalBranchPoints[skel]]


enter image description here



Another possibility is to use



skel1 = Pruning[Thinning[im, Method -> "MedialAxis"], 10];


as a start then smoothen the result using 



skel2 = Thinning@Dilation[skel1, 5]


so that MorphologicalBranchPoints would not give false results.



HighlightImage[skel2, MorphologicalBranchPoints[skel2]]


enter image description here







share|improve this answer














share|improve this answer



share|improve this answer








edited Feb 18 at 15:10

























answered Feb 18 at 14:38









SzabolcsSzabolcs

161k14438936




161k14438936












  • $begingroup$
    Great ! Thanks a lot.
    $endgroup$
    – Valacar
    Feb 18 at 15:09


















  • $begingroup$
    Great ! Thanks a lot.
    $endgroup$
    – Valacar
    Feb 18 at 15:09
















$begingroup$
Great ! Thanks a lot.
$endgroup$
– Valacar
Feb 18 at 15:09




$begingroup$
Great ! Thanks a lot.
$endgroup$
– Valacar
Feb 18 at 15:09











7












$begingroup$

I notice there's a faint pink background that seems like a natural boundary. I've highlighted it to showcase this:



enter image description here



We can extract this curve and use it as the original boundary:



im = Import["https://i.stack.imgur.com/7Ck2S.png"];

mask = FillingTransform[Thinning[Binarize[ColorReplace[im, White -> Black, .055], 0]], CornerNeighbors -> True]


enter image description here



And the simply call MorphologicalBranchPoints:



skel = Thinning[mask];

HighlightImage[skel, MorphologicalBranchPoints[skel], 1]


enter image description here






share|improve this answer











$endgroup$













  • $begingroup$
    This is really clean!
    $endgroup$
    – Valacar
    Feb 19 at 9:24










  • $begingroup$
    by the way I think it's Thinning[mask] in this case, isn't it?
    $endgroup$
    – Valacar
    Feb 19 at 12:26










  • $begingroup$
    @Valacar Yes. I made the correction, thank you.
    $endgroup$
    – Chip Hurst
    Feb 19 at 12:36


















7












$begingroup$

I notice there's a faint pink background that seems like a natural boundary. I've highlighted it to showcase this:



enter image description here



We can extract this curve and use it as the original boundary:



im = Import["https://i.stack.imgur.com/7Ck2S.png"];

mask = FillingTransform[Thinning[Binarize[ColorReplace[im, White -> Black, .055], 0]], CornerNeighbors -> True]


enter image description here



And the simply call MorphologicalBranchPoints:



skel = Thinning[mask];

HighlightImage[skel, MorphologicalBranchPoints[skel], 1]


enter image description here






share|improve this answer











$endgroup$













  • $begingroup$
    This is really clean!
    $endgroup$
    – Valacar
    Feb 19 at 9:24










  • $begingroup$
    by the way I think it's Thinning[mask] in this case, isn't it?
    $endgroup$
    – Valacar
    Feb 19 at 12:26










  • $begingroup$
    @Valacar Yes. I made the correction, thank you.
    $endgroup$
    – Chip Hurst
    Feb 19 at 12:36
















7












7








7





$begingroup$

I notice there's a faint pink background that seems like a natural boundary. I've highlighted it to showcase this:



enter image description here



We can extract this curve and use it as the original boundary:



im = Import["https://i.stack.imgur.com/7Ck2S.png"];

mask = FillingTransform[Thinning[Binarize[ColorReplace[im, White -> Black, .055], 0]], CornerNeighbors -> True]


enter image description here



And the simply call MorphologicalBranchPoints:



skel = Thinning[mask];

HighlightImage[skel, MorphologicalBranchPoints[skel], 1]


enter image description here






share|improve this answer











$endgroup$



I notice there's a faint pink background that seems like a natural boundary. I've highlighted it to showcase this:



enter image description here



We can extract this curve and use it as the original boundary:



im = Import["https://i.stack.imgur.com/7Ck2S.png"];

mask = FillingTransform[Thinning[Binarize[ColorReplace[im, White -> Black, .055], 0]], CornerNeighbors -> True]


enter image description here



And the simply call MorphologicalBranchPoints:



skel = Thinning[mask];

HighlightImage[skel, MorphologicalBranchPoints[skel], 1]


enter image description here







share|improve this answer














share|improve this answer



share|improve this answer








edited Feb 19 at 12:35

























answered Feb 19 at 4:05









Chip HurstChip Hurst

21.7k15790




21.7k15790












  • $begingroup$
    This is really clean!
    $endgroup$
    – Valacar
    Feb 19 at 9:24










  • $begingroup$
    by the way I think it's Thinning[mask] in this case, isn't it?
    $endgroup$
    – Valacar
    Feb 19 at 12:26










  • $begingroup$
    @Valacar Yes. I made the correction, thank you.
    $endgroup$
    – Chip Hurst
    Feb 19 at 12:36




















  • $begingroup$
    This is really clean!
    $endgroup$
    – Valacar
    Feb 19 at 9:24










  • $begingroup$
    by the way I think it's Thinning[mask] in this case, isn't it?
    $endgroup$
    – Valacar
    Feb 19 at 12:26










  • $begingroup$
    @Valacar Yes. I made the correction, thank you.
    $endgroup$
    – Chip Hurst
    Feb 19 at 12:36


















$begingroup$
This is really clean!
$endgroup$
– Valacar
Feb 19 at 9:24




$begingroup$
This is really clean!
$endgroup$
– Valacar
Feb 19 at 9:24












$begingroup$
by the way I think it's Thinning[mask] in this case, isn't it?
$endgroup$
– Valacar
Feb 19 at 12:26




$begingroup$
by the way I think it's Thinning[mask] in this case, isn't it?
$endgroup$
– Valacar
Feb 19 at 12:26












$begingroup$
@Valacar Yes. I made the correction, thank you.
$endgroup$
– Chip Hurst
Feb 19 at 12:36






$begingroup$
@Valacar Yes. I made the correction, thank you.
$endgroup$
– Chip Hurst
Feb 19 at 12:36




















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