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Re: RGB to binary problem

Posted by Tal Shprung on Jun 25, 2008; 5:05am
URL: http://imagej.273.s1.nabble.com/RGB-to-binary-problem-tp3695771p3695775.html

I am using only the red channel in my images. I guess that although the blue
and green channels are empty, when converting directly to binary they make
some noise  for some reason.

On Tue, Jun 24, 2008 at 9:44 PM, Doug S <[hidden email]> wrote:

> Tal or Wayne,
>   about using just the red channel,
> is that specifically for the type of images you are using
> ( e.g they have better contrast in that channel )
> or is there something inherently better about the red channel
> as opposed to the blue or green?
> Doug
>
>
> Tal Shprung wrote:
>
>> Wayne Rasband told me to do RGB split and then use the red channel (as my
>> pictures are with only red color).
>>
>> I have tried it and saw that it gives the cleanest binary picture
>> (compared
>> to going straight to binary or even to 32bit or 8bit grayscale and then
>> binary).
>> Thank you all.
>>
>> Tal
>>
>> On Tue, Jun 24, 2008 at 9:02 PM, Albert Cardona <
>> [hidden email]>
>> wrote:
>>
>>
>>
>>>  I have tried to change RGB images to binary by going to process ->
>>> binary
>>>
>>>
>>>> ->
>>>> make binary, but in this way I get a lot of background noise in many of
>>>> the
>>>> images.
>>>>
>>>> By chance I have done the process going through 32-bit gray scale (image
>>>> ->
>>>> type -> 32-bit) and than "make binary" and saw that it bypasses the
>>>> background noise problem.
>>>>
>>>> Have anyone else encountered this phenomenon? Are there any comments?
>>>>
>>>>
>>>>
>>>>
>>> I suspect your RGB image goes to 8-bit color first (which is terrible for
>>> any quantification, since pixel intensities no longer correlate to color
>>> intensity), and then to binary.
>>>
>>> RGB to 32 bit will do the right thing: create a luminance image and put
>>> it
>>> into a float array.
>>>
>>> You can see what RGB really is if you convert the RGB to HSB, and then
>>> use
>>> the 'B' slice of the 3-slice stack (the Brightness channel) which is
>>> essentially the grayscale version of your image (Hue and Saturation
>>> containing the color information.)
>>>
>>> Albert
>>>
>>> --
>>> Albert Cardona
>>> http://www.mcdb.ucla.edu/Research/Hartenstein/acardona
>>>
>>>
>>>
>>
>>
>>
>