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A new and efficient method for casts recognition in urinary sediment microscopic images is proposed in this paper. It combines the shape and texture characteristics of casts, and accordingly, consists of two steps. In the first step, the casts' tube-like shape feature is expressed by a modified method stems from the traditional one which is based on the minimum bounding rectangle(MBR). Instead of...
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 appearance of cast cells in urine sediment is an essential sign of serious renal or urinary tract diseases. However, due to uneven illumination, low contrast against the background and complicated components of the microscopic urine sediment images, detection and recognition of cast cells in former study can not be considered sufficient. In this paper, an efficient approach for casts detecting...
This paper presents a coarse-to-fine particle segmentation strategy to extract particles from microscopic urinary images within two stages, coarse stage and fine stage. In coarse stage, to locate particles in a wide range of images including the low contrast, the unevenly illuminated, etc, we develop 4-direction variance mapping followed by an adaptive thresholding method. Within this stage, particles...
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