Automated classfication of particles in urinary sediment

The particles in urinary microscopic images are hard to classify because of noisy background and strong variability of objects in shape and texture. In order to overcome these difficulties, firstly, a new method of texture feature extraction using the distance mapping based on a set of local grayvalue invariants is introduced and the feature is robust to the shift and rotation. Secondly, we reduce the high dimensional feature into a lower dimensional space using PCA. Thirdly, a multiclass SVM is applied to classify 5 categories of particles after trained them reasonably. Finally the experiment results achieve an average of accuracy of 90.02% and a F1 value of 90.44%.