Automatic detecting and recognition of casts in urine sediment images

Abstract

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 and recognition in urine sediment images is proposed. It consists of three stages: Firstly, 4-direction variance mapping image is acquired from gray scale image. Secondly, we obtain binary image by applying an improved adaptive bi-threshold segmentation algorithm to the above mapping image. In the last stage, five texture and shape characteristics of casts are extracted from both gray scale image and binary image. Based on these characteristics, we develop an decision-tree classifier to distinguish casts from other particles in the image. Experimental results show that our method produces satisfactory segmentation, achieves an easy-implemented, time-saving classifier and has improved recognition performance.