Abstract
In the past, cast cells in urine sediment were recognized and sorted mainly by human. We proposed an automatic method for classification and recognition of particles mainly white blood cell (WBC) and red blood cell (RBC) in urinary sediment. It is composed of three stages: First, Original urinary sediment microscopic images are transformed into binary image by image pretreatment including median filtering, color image conversion to gray scale image and image segmentation. Second, we select and extract some features as feature vectors for classification and recognition. In the last, eleven texture and shape characteristics of casts are extracted from both gray scale image and binary image. Based on these characteristics, we develop an SVM classifier to distinguish casts from other particles in the image. Experimental results show that our method achieves an easy-implemented classifier and has good recognition performance.
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Zhou, Y., Zhou, H. (2012). Automatic Classification and Recognition of Particles in Urinary Sediment Images. In: He, X., Hua, E., Lin, Y., Liu, X. (eds) Computer, Informatics, Cybernetics and Applications. Lecture Notes in Electrical Engineering, vol 107. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-1839-5_116
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DOI: https://doi.org/10.1007/978-94-007-1839-5_116
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