This work focuses on the segmentation and counting of peripheral blood smear particles which plays a vital role in
medical diagnosis. Our approach profits from some powerful processing techniques. Firstly, the method used for
denoising a blood smear image is based on the Bivariate wavelet. Secondly, image edge preservation uses the Kuwahara
filter. Thirdly, a new binarization technique is introduced by merging the Otsu and Niblack methods. We have also
proposed an efficient step-by-step procedure to determine solid binary objects by merging modified binary, edged
images and modified Chan-Vese active contours. The separation of White Blood Cells (WBCs) from Red Blood Cells
(RBCs) into two sub-images based on the RBC (blood’s dominant particle) size estimation is a critical step. Using
Granulometry, we get an approximation of the RBC size. The proposed separation algorithm is an iterative mechanism
which is based on morphological theory, saturation amount and RBC size. A primary aim of this work is to introduce an
accurate mechanism for counting blood smear particles. This is accomplished by using the Immersion Watershed
algorithm which counts red and white blood cells separately. To evaluate the capability of the proposed framework,experiments were conducted on normal blood smear images. This framework was compared to other published
approaches and found to have lower complexity and better performance in its constituent steps; hence, it has a better
overall performance
