Particle counting using image processing in python

Is there a good algorithm for detecting particles when the background intensity changes? For example, if I have the following image:

Is there a way to count small white particles, even with a clearly distinguishable background that appears in the lower left corner?

To be a little clearer, I would like to tag the image and count the particles by an algorithm that considers these particles significant:

I tried a lot of things with the modules PIL, cv, scipy, numpy, etc. I got some tips from this very similar SO question , and at first glance it seems like you can make a simple threshold:

im = mahotas.imread('particles.jpg')
T = mahotas.thresholding.otsu(im)

labeled, nr_objects = ndimage.label(im>T)
print nr_objects
pylab.imshow(labeled)

but due to the changing background you will get the following:

, , , :

import numpy as np
import scipy
import pylab
import pymorph
import mahotas
from scipy import ndimage
import cv


def detect_peaks(image):
    """
    Takes an image and detect the peaks usingthe local maximum filter.
    Returns a boolean mask of the peaks (i.e. 1 when
    the pixel value is the neighborhood maximum, 0 otherwise)
    """

    # define an 8-connected neighborhood
    neighborhood = ndimage.morphology.generate_binary_structure(2,2)

    #apply the local maximum filter; all pixel of maximal value 
    #in their neighborhood are set to 1
    local_max = ndimage.filters.maximum_filter(image, footprint=neighborhood)==image
    #local_max is a mask that contains the peaks we are 
    #looking for, but also the background.
    #In order to isolate the peaks we must remove the background from the mask.

    #we create the mask of the background
    background = (image==0)

    #a little technicality: we must erode the background in order to 
    #successfully subtract it form local_max, otherwise a line will 
    #appear along the background border (artifact of the local maximum filter)
    eroded_background = ndimage.morphology.binary_erosion(background, structure=neighborhood, border_value=1)

    #we obtain the final mask, containing only peaks, 
    #by removing the background from the local_max mask
    detected_peaks = local_max - eroded_background

    return detected_peaks

im = mahotas.imread('particles.jpg')
imf = ndimage.gaussian_filter(im, 3)
#rmax = pymorph.regmax(imf)
detected_peaks = detect_peaks(imf)
pylab.imshow(pymorph.overlay(im, detected_peaks))
pylab.show()

, :

, , , -, , ( 2, 3 4):

, , :

, .

EDIT: : , :

import cv2
import pylab
from scipy import ndimage

im = cv2.imread('particles.jpg')
pylab.figure(0)
pylab.imshow(im)

gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (5,5), 0)
maxValue = 255
adaptiveMethod = cv2.ADAPTIVE_THRESH_GAUSSIAN_C#cv2.ADAPTIVE_THRESH_MEAN_C #cv2.ADAPTIVE_THRESH_GAUSSIAN_C
thresholdType = cv2.THRESH_BINARY#cv2.THRESH_BINARY #cv2.THRESH_BINARY_INV
blockSize = 5 #odd number like 3,5,7,9,11
C = -3 # constant to be subtracted
im_thresholded = cv2.adaptiveThreshold(gray, maxValue, adaptiveMethod, thresholdType, blockSize, C) 
labelarray, particle_count = ndimage.measurements.label(im_thresholded)
print particle_count
pylab.figure(1)
pylab.imshow(im_thresholded)
pylab.show()

:

( )

( )

60.