Abslracf-A technique for automating the differential count of blood is presented. The proposed system takes as input, color images of stained peripheral blood smears and identifies the class of each of the White Blood Cells (WBC), in order to determine the count of cells in each class. The process involves segmentation, feature extraction and classification. WBC segmentation is a two-step process carried out on the HSV-equivalent of the image, using K-Means clustering followed by EM-algorithm. curacy of 97% is obtained using Neural networks, followed by 94% using SVM
Neelam et al. REF have suggested a model for segmentation of WBCs which is a two-step process performed on the HSV-equivalent of the image.
Automation of differential blood count
TENCON 2003. Conference on Convergent Technologies for Asia-Pacific Region
TENCON 2003. Conference on Convergent Technologies for Asia-Pacific Region
Computer Science