This article is about implementing a hematological analysis through computer vision algorithms. This type of analysis is one of the basic analyses providing huge amount information about patient and his state. We propose a pipeline with a few steps for image preprocessing thus image become more contrast and noiseless. At first image color space converting – so we separate a luminance channel and ignore other channels (due to source image features). Then we blur image with Gaussian filter and apple CLAHE filter for contrast improvement, so background pixels form more homogenous areas and become less bright in comparison to cell’s pixels. The next step is background removal and image binarization based on Otsu algorithm for border pixel luminance level detection. Afterwards we extract an array of contours from binary image and use this array as an input source for Watersched algorithm. As a result, we have a color image where every single class of object has its own color and an array of object. This array then used as a source for cells diameters distribution histogram – a Price-Jones curve. All described steps implemented in Python 2.7 with OpenCV and Seaborn libraries.