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The microscopic analysis of the urinary sediment is important in making diagnoses for a variety of diseases, including urinary tract infection, urinary tract tumors, occult glomerulonephritis, and interstitial nephritis. A typical automated system acquires images of urinary sediment by employing a CCD camera, and then detects and recognizes the distinct particles automatically from these images. Automated recognition of these particles represents a significant challenge due to poor image resolution, strong variability of particle shape and size, and challenges associated with detection of particles in the presence of noisy backgrounds. In this paper, we present a novel method for urine particle classification based on the use of local descriptors coupled with regression based decision fusion. Specifically, DAISY descriptors have been used to capture the textural characteristics of each particle and subjected to dimensionality reduction across three linear subspaces to increase the diversity in decision making along with lowering the "curse of dimensionality". Classification in each subspace is based on computing a similarity score, which is then fused through support vector regression to obtain a final classification. The approach is applied to both brightfield and multispectral data to ascertain the benefits of multispectral imaging for urine analysis. Urine particles analyzed included crystals, casts and blood cells, and the results obtained show an average classification accuracy of 92.6% for 6 classes of urinary particles.