A fast semi-automatic method for classification and counting the number and types of blood cells in an image
This incremental method addresses the need for efficient blood cell analysis in medical diagnostics.
The authors tackled the problem of counting and classifying blood cells in images by proposing a fast semi-automatic method that uses thresholding, Hough transform, and template matching, achieving accurate white cell counts and red cell counts with small errors.
A novel and fast semi-automatic method for segmentation, locating and counting blood cells in an image is proposed. In this method, thresholding is used to separate the nucleus from the other parts. We also use Hough transform for circles to locate the center of white cells. Locating and counting of red cells is performed using template matching. We make use of finding local maxima, labeling and mean value computation in order to shrink the areas obtained after applying Hough transform or template matching, to a single pixel as representative of location of each region. The proposed method is very fast and computes the number and location of white cells accurately. It is also capable of locating and counting the red cells with a small error.