| Re: finding boundary in image having multiple region of interest [message #93591 is a reply to message #93590] |
Wed, 31 August 2016 03:46  |
gunvicsin11
Messages: 93
Registered: November 2012
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Member |
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On Wednesday, August 31, 2016 at 3:07:14 PM UTC+5:30, Helder wrote:
> Hi,
> I can't tell if what you did is correct or not, but if you want to convert one-dimensional subscript to two dimensional, then array_indices is the way to go.
> For some examples, to learn how to use it, have a look at the help pages:
> http://www.harrisgeospatial.com/docs/ARRAY_INDICES.html
>
> I also think that the label_region page has an example on how to use the reverse_indices of the histogram function to retrieve one-dimensional subscripts:
> http://www.harrisgeospatial.com/docs/LABEL_REGION.html
> I think the last line of the example is the most important one:
> img[r[r[i]:r[i+1]-1]]
>
> The histogram function help page also has some more info on how to use the reverse indices:
> http://www.harrisgeospatial.com/docs/histogram.html
>
> Good luck,
> Helder
>
>
> On Wednesday, August 31, 2016 at 8:21:46 AM UTC+2, sin wrote:
>> On Tuesday, August 30, 2016 at 2:22:18 PM UTC+5:30, Helder wrote:
>>> On Tuesday, August 30, 2016 at 8:07:48 AM UTC+2, sin wrote:
>>>> Hi all,
>>>> I need to select roi having combined pixel area above 3493pixels and intensity above 1.25.
>>>>
>>>> I tried using find_boundary which gives the pixel area for pixels above the threshold for one roi that we give as input. But in one image I have multiple rois to be selected. so find_boundary will not work.
>>>>
>>>> I have several images like this.
>>>>
>>>> So do anyone have any idea to do this.
>>>>
>>>> The threshold should be the intensity should be more than 1.25 and the combined pixel area should be greater than 3493 pixels.
>>>>
>>>> thanks
>>>
>>> Hi,
>>> I don't understand why you tried find boundary. But I think what you're looking for is a combination of "greater then" and label_region.
>>>
>>> subImage = myImage gt 1.25
>>> lr = label_region(subImage)
>>>
>>> then use histogram to identify the regions and look for the one's with more than 3493 pixels. In case of doubt, follow the example given for label_region:
>>> http://www.harrisgeospatial.com/docs/LABEL_REGION.html
>>>
>>> Here is what is of interest for you:
>>>
>>> h = histogram(lr, REVERSE_INDICES=r)
>>>
>>> ; Print the mean and standard deviation of each region
>>> FOR i=0, N_ELEMENTS(h)-1 DO if h[i] gt 3493 then $
>>> PRINT, 'This region ', i, ', has a population greater than 3493 and has = ', h[i], $
>>> ', elements. The indices of this region are between ', r[i],' and ', r[i+1]-1
>>>
>>> I hope this helps.
>>>
>>> Cheers,
>>> Helder
>>
>> Thanks a lot helder, it is very helpful.
>> But I couldnt understand how to get array x,y coordinates from the indices r(i).
>> can i use array_indices for this purpose.
>> I tried but i am not sure whether it is correct or not.
>>
>> thanks
Thanks a lot helder
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