On Wed, Feb 2, 2011 at 8:22 AM, Asmi Shah wrote:
> Hi all,
> It seems that using 64 bit python is the solution. But the thing is i would
> compile my code and wanna distribute it to the clients.. and that is the
> only reason why i want to work on 32 bit system. Sturla, how I can make it
> sure th
Hi,
On Tue, Feb 1, 2011 at 6:39 AM, Asmi Shah wrote:
> Thanks a lot Friedrich and Chris.. It came in handy to use PIL and numpy..
> :)
> @Zach, m aware of the poor handling of 16bit images in PIL, for that I am
> using imagemagick to convert it into 8 bit first and then PIL for rest of
> the proc
I've been done that but with CT and MRI dicom files, and the cool
thing is that with numpy I can do something like this:
# getting axial slice
axial = slices[n,:,:]
# getting coronal slice
coronal = slices[:, n, :]
# getting sagital slice
sagital = slices[:,:, n]
On Sun, Jan 30, 2011 at 5:29 PM
I have the same problem here. I'm using numexpr 1.4.1 and numpy 1.3.0.
On Mon, Jan 24, 2011 at 4:19 PM, John Salvatier
wrote:
> Forgot to mention that I am using numexpr 1.4.1 and numpy 1.5.1
>
> On Mon, Jan 24, 2011 at 9:47 AM, John Salvatier
> wrote:
>>
>> Hello,
>> I have discovered a strange
On Mon, Jan 10, 2011 at 11:53 AM, totonixs...@gmail.com
wrote:
> Hi all,
>
> I have this problem: Given some point draw a circle centered in this
> point with radius r. I'm doing that using numpy this way (Snippet code
> from here [1]):
>
>>>> # Creat
Hi all,
I have this problem: Given some point draw a circle centered in this
point with radius r. I'm doing that using numpy this way (Snippet code
from here [1]):
>>> # Create the initial black and white image
>>> import numpy as np
>>> from scipy import ndimage
>>> a = np.zeros((512, 512)).asty
On Fri, Dec 31, 2010 at 7:44 PM, Gideon wrote:
> I noticed that 1.5.1 was released, and sourceforge is suggesting I use
> the package numpy-1.5.1-py2.6-python.org-macosx10.3.dmg. However, I
> have an OS X 10.6 machine.
>
> Can/should I use this binary?
>
> Should I just compile from source?
I su
On Thu, Dec 2, 2010 at 11:14 AM, Zachary Pincus wrote:
>> mask = numpy.zeros(medical_image.shape, dtype="uint16")
>> mask[ numpy.logical_and( medical_image >= lower, medical_image <=
>> upper)] = 255
>>
>> Where lower and upper are the threshold bounds. Here I' m marking the
>> array positions whe
Hi all,
I' m developing a medical software named InVesalius [1], it is a free
software. It uses numpy arrays to store the medical images (CT and
MRI) and the mask, the mask is used to mark the region of interest and
to create 3D surfaces. Those array generally have 512x512 elements.
The mask is cr
