No abstract

Blood cell counting is an important medical test to help medical sta®s diagnose various symptoms and diseases. An automatic segmentation of complex overlapping erythrocytes based on seed prediction in microscopic imaging is proposed. The four main innovations of this research are as follows: (1) Regions of erythrocytes extracted rapidly and accurately based on the G component.(2) K-means algorithm is applied on edge detection of overlapping erythrocytes. (3) Traces of erythrocytes' biconcave shape are utilized to predict erythrocyte's position in overlapping clusters. (4) A new automatic counting method which aims at complex overlapping erythrocytes is presented. The experimental results show that the proposed method is e±cient and accurate with very little running time. The average accuracy of the proposed method reaches 97.0%.

Red blood cell (RBC) counting is a standard medical test that can help diagnose various conditions and diseases. Manual counting of blood cells is highly tedious and time consuming. However, new methods for counting blood cells are customary employing both electronic and computer-assisted techniques. Image segmentation is a classical task in most image processing applications which can be used to count blood cells in a microscopic image. In this research work, an approach for erythrocytes counting is proposed. We employed a classi¯cation before counting and a new segmentation idea was implemented on the complex overlapping clusters in a microscopic smear image. Experimental results show that the proposed method is of higher counting accuracy and it performs much better than most counting algorithms existed in the situation of three or more RBCs overlapping complexly into a group. The average total erythrocytes counting accuracy of the proposed method reaches 92.9%.