Development algorithm to count blood cells in urine sediment using ANN and Hough Transform

Nowadays, microscopic is used in several laboratories for detect cells or parasite by technician. Especially testing in urine sediment is important for the patients who are abnormal about urinary tract. Constantly, the appearance of red blood cells, white blood cells, crystals, bacteria and other microorganisms in urine sediment's patients is more important information for diagnosis. This paper proposes the segmentation and detection of RBCs and WBCs in urine sediment images. The process of algorithm consists of three main parts. First step is segmentation by using feedforward backpropagation of Artificial Neural Network applied on the HSV color model image of urine sediment examination. The next step is eliminating noise by morphology operations. The last step is detection RBCs and WBCs by using Circle Hough Transform. Experimental results show the average percentage of error of RBCs and WBCs detection, 5.28 and 8.35 respectively.