Question

How can I improve the speed of this function?

def foo(mri_data, radius):

    mask = mri_data.copy()

    ny = len(mri_data[0,:])
    nx = len(mri_data[:])

    for y in xrange(0, ny):
        for x in xrange(0, nx):
            if (mri_data[x-radius:x+radius,y-radius:y+radius] != 1.0).all():
                mask[x,y] = 0.0                    
    return mask.copy() 

It takes in image slices in the form of a numpy array. Iterates through each pixels and tests a bounding box around that pixel. If no values in the box are equal to 1 than we discard that pixel by setting it to 0.

I’ve been told I can use numpy.convolve but I am uncertain how this relates.

EDIT: The images values are in binary range so lowest value is 0.0 and max value is 1.0. With values in between ex: 0.767.

Answer 1

One of the cases where you can abuse convolution. I wouldn’t use it, but the boundaries are otherwise tedious…

from scipy.ndimage import convolve

not_one = (mri_data != 1.0) # are you sure you want to compare with float like that?!

conv = convolve(not_one, np.ones((2*radius, 2*radius)))
all_not_one = (conv == (2*radius)**2)

mask[all_not_one] = 0

Should do the same thing really…