Anastasiya Derkachova,, Gennadiy Derkachov, Krystyna Kolwas, Agnieszka Sozańska Institute of Physics, Polish Academy of Sciencies, Al. Lotników 32/46, , Warszawa CCD camera Microscope with Hoffman modulation contrast (Nikon Eclipse TS100-F) The microscope table was stabilized at 37°C,to unsure the optimal survival temperature for the bovine spermatozoa under study. Water bath with the thermoregulations system for thawing and incubation of sperm cells. The methods we used for image evaluation is based on the numerically supported phase contrast microscope technique. It allows for cheap and objective comparison of sperm motility of different sperm samples, as well for analysis of the sperm cells tracks, and velocities. The method relies on a numerical processing of the optical contrast of the sperm images registered as a movie by a CCD camera integrated with the eyepiece of the microscope with Hoffman modulation contrast (Nikon Eclipse TS100-F). Every movie was analyzed frame by frame. The unwanted background is eliminated by standard Matlab function in several steps: assignment of the background level (Fig 1b); subtraction of the background from the image. As a result we obtain the image in grayscale of low intensity values (Fig.1c) ; magnification of the intensity values of the grayscale image (Fig 1d); conversion of the grayscale image to the binary image. To the all illuminated pixels of the grayscale image (Fig 1d) the value 1 (white color) is assigned. To the all remaining pixels with the value 0 (black color) is ascribed (Fig.2b). Fig. 1. An example frame of a movie: (a) before numerical processing; (b) after assigning the background level; (c) after subtracting the background from the image and (d) after expanding the intensity values in a grayscale image. a) b) c) d) Fig. 2. The frame of a movie : (a) before and (b) after numerical processing. a) b) Fig. 3. Illustration of the numerical processing of the successive frames of a movie the final result for the superimposed frames. All of the binary images are summarized (Fig. 3, the left hand- side). As a result we obtain the binary image (Fig. 3 on the right) and the underlying data matrix containing information about the dynamics of the sperm sample (Fig.4). Fig. 4. The 3D images of the final binary image. Fig. 5. The superimposed frames of a movie after numerical processing. Fig. 6. Selection of a sperm cell track allowing the velocity evaluation from the track intensity. Fig. 7. Distribution of the tracks frequency versus intensity (left-hand side), and of the sperm cells velocity (right-hand side) for samples with motility 50% as a function of time. Fig. 8. Distribution of the tracks frequency versus intensity (left-hand side), and of the sperm cells velocity (right-hand side) for samples with motility 56% as a function of time. Fig. 9. Temporal evolution of the velocity (left-hand side) and motility (right-hand side) for samples with the initial sperm motility 50%, 56% and 63%. Fig. 9. The velocity (left-hand side) and the motility (right-hand side) time evolution for samples with the initial sperm motility 50%, 56% and 63%.