Texture Segmentation of Urinary Sediment Image based on a Weighted Gaussian Mixture Model with Markov Random Fields

Jiang, Xiaoming; Chen, Fuqu; Chen, Qianqian; Si, Miaomiao; Wang, Wei

doi:10.1145/3239264.3239276

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…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. They selected sum average feature derived from the spatial gray co-occurrence matrix for the classification in the neighborhood window.…”

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%

Research on Urine Sediment Images Recognition Based on Deep Learning

et al. 2019

IEEE Access

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A review on various methods for recognition of urine particles using digital microscopic images of urine sediments

Suhail¹,

Brindha²

2021

Biomedical Signal Processing and Control

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Deep Multi-Modal Fusion Model for Identification of Eight Different Particles in Urinary Sediment

Tuncer,

Çınar,

Erkuş

et al. 2024

Applied Computer Systems

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Urine sediment examination (USE) is an essential aspect in detecting urinary system diseases, and it is a prerequisite for diagnostic procedures. Urine images are complex, containing numerous particles, which makes a detailed analysis and interpretation challenging. It is crucial for both patients and medical professionals to conduct urine analysis automatically, quickly and inexpensively, without compromising reliability. In this paper, we present a deep multi-modal fusion system, commonly employed in artificial intelligence, capable of automatically distinguishing particles in urine sediment. To achieve this objective, we first created a new dataset comprising erythrocytes, leukocytes, yeast, epithelium, bacteria, crystals, cylinders, and other particles (such as sperm). The data were gathered from urinalysis requests made between July 2022 and September 2022 at the biochemistry laboratory of Fethi Sekin Medical Center Hospital. A dataset containing 8509 images was compiled using the Optika B293PLi microscope with trinocular brightfield. We propose a 5-step process for detecting particles in the dataset using a multi-modal fusion deep learning model: i) The obtained images were augmented by applying affine transformation. ii) To distinguish images, we opted for ResNet18 and ResNet50 models, which yielded high performance in medical data. iii) Feature vectors from both models were fused to generate more consistent, accurate, and useful particle features. iv) We employed ReliefF, Neighborhood Component Analysis (NCA), and Minimum-Redundancy Maximum-Relevancy (mRMR) feature selection methods, widely used to determine features that maximise particle discrimination success. v) In the final step, Support Vector Machine (SVM) was utilised to distinguish the particles. The results demonstrate that the highest accuracy value achieved is 98.54 % when employing the ReliefF algorithm. Contributions of the study include eliminating standardisation differences in manual microscopy, achieving high accuracy in particle discrimination, offering an artificial intelligence-based system applicable in laboratory environments, and providing the dataset as educational and practical material for biochemistry professionals.

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Texture Segmentation of Urinary Sediment Image based on a Weighted Gaussian Mixture Model with Markov Random Fields

Cited by 3 publications

References 9 publications

Research on Urine Sediment Images Recognition Based on Deep Learning

Research on Urine Sediment Images Recognition Based on Deep Learning

A review on various methods for recognition of urine particles using digital microscopic images of urine sediments

Deep Multi-Modal Fusion Model for Identification of Eight Different Particles in Urinary Sediment

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