Applied Medical Informatics in the Detection and Counting of Erythrocytes and Leukocytes through an Image Segmentation Algorithm
More than half of the medical decisions are made on the basis of laboratory tests, which are essential to complete diagnosis or to refer the patient to more specific tests. In this context, the blood count is the most requested laboratory test. Even though there is a wide variety of CBC equipment, many are similar in cost. The automated methodologies present high speed and high accuracy, however, the high cost is often incompatible with the cost of living of people living in less favored countries. In this way, it is essential to develop methodologies that reduce the cost of the blood count. The present study is based on the development of a laboratory medical algorithm for the detection and counting of erythrocytes and leukocytes in digital blood smear images. The algorithm was based on the Hough Transform and detection of objects by coloration. The development of the algorithm, as well as its performance, were performed in the virtual environment of Matlab software. The experiments were conducted through 10 digital images acquired in open access platforms with later analysis of sample execution times through the "tic toc" function. The results of the quantifications were expressed separately. The methodology developed showed high accuracy (90%) as well as low time to execute each of the images analyzed, with the average execution time being less than 2 seconds. Therefore, this study can be considered the first step in the accomplishment of hemograms with low cost, greater accessibility, and speed without the loss of reliability of the method.
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