An Automatic Algorithm for Image Segmentation in Urine Sediment Examination
Limin Wang · Hangtian yixue yu yixue gongcheng · 2007
Objective To study an accurate algorithm for automatic image segmentation in urine sediment examination.Methods The Mumford-Shah model and level set method were integrated and used to segment the urine sediment image.The algorithm was evaluated by simulation and real data experiment with the improved version of Zhang's criterion.Results The Mumford-Shah model based Level Set algorithm could eliminate the over-segment produced by the Level Set,and always had a lowest as compared with the other three algorithms,such as expectation maximization(EM),region grow and watershed.Timing results showed that the narrow band Level Set algorithm had a highest computational expense(1.8×104 s)while the Mumford-Shah model based Level Set algorithm was much faster(5.42 s).Conclusion The Mumford-Shah model based Level Set algorithm can achieve urine sediment examinations accurately with both fast speed and strong robustness to the noise.