Research on urinary micro-particles image feature extraction and analysis method

Zheng, Xin; Zhou, Xiaomou; Zheng, Mingjie

doi:10.1145/3317640.3317650

Cited by 2 publications

(1 citation statement)

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“…Sun et al [26] adopt ACF which are variant and discriminative, combing improved soft-cascade adaboost classifier for RBCs detection in urine sediment micrograph. Zheng et al [27] obtained the feature of urine sediment images according to the chain code method. Jiang et al [28] proposed a method for the segmentation of urine sediment images using the magnification of 20-fold microscopy based on Markov model.…”

Section: B Automatic Recognition Of Urine Sediment Imagesmentioning

confidence: 99%

Research on Urine Sediment Images Recognition Based on Deep Learning

et al. 2019

IEEE Access

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Detection of urine sediment microscopic images of human urine samples plays an important part in vitro examination. Doctors usually use automatic urine sediment analyzer to assist manual examine. At present, automatic urine sediment analyzers mostly use traditional method of artificial feature extraction to recognize urine sediment images. However, traditional image processing methods based on the selection and combination of feature operators and classifiers require a lot of work and subjective experience for engineers in the implementation process. It's also difficult to deal with urine sediment images recognition tasks with large scale categories, and particles in some different categories are often confused in recognition using traditional image processing methods, such as red blood cells (RBCs) and white blood cells (WBCs). In this paper, a combination convolution neural network (CNN) recognition method with area feature algorithm is proposed. The disadvantage that CNN can weaken the area feature of input image is solved by area feature algorithm (AFA) proposed in this paper. The network models which use 300,000 urine sediment images for training can quickly and accurately recognize 10 categories of urine sediment images, and several confusing categories' recognition indexes are remarkably improved. The test accuracy in the test set reached 97%. INDEX TERMS Automatic diagnosis, deep learning, image processing, urine sediment.

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Section: B Automatic Recognition Of Urine Sediment Imagesmentioning

confidence: 99%