Exploiting aggregate channel features for urine sediment detection

Urine sediment examination refers to the use of microscopes to examine various tangible components in urine sediment, e.g. red blood cells (RBCs), white blood cells (WBCs), tube, and crystal, etc., having a very important role in infectious diseases and circulatory diseases diagnosis. The traditional method about urine sediment analysis depends on the observation of medical staffs. So the workload is particularly large and inefficient, and relevant staff need to own some experience. Recently, the automation of urine sediment analysis can be realized. However, due to the complexity of the urine sediment microscopic image, the accuracy and efficiency of the automatic recognition for the tangible components are still very low. To solve this problem, we investigate channel features to urine sediment detection which include diverse feature types like color channel features and gradient magnitude, etc. We propose aggregate channel features plus (ACF+) detector which is based on aggregate channel features (ACF) for urine sediment detection. We adopt improved Adaboost classifier. The input image does not require any preprocessing and the specific ingredients such as RBCs can be detected directly with a high precision and efficiency. On the testing set, our proposed ACF+ detector suppresses several competitive baselines e.g. Support Vector Machine (SVM) combined with Histogram of Oriented Gradient (HOG), vanilla ACF, and ACDS. In terms of speed, it runs 3FPS on 2592?×?2048 images.