Urinary sediment detection method based on unbalanced Fisher discriminant analysis

Abstract

The invention discloses a urinary sediment detection method based on unbalanced local Fisher discriminant analysis. The urinary sediment detection method includes the steps: firstly, extracting aggregation channel features from an input urinary sediment visible component image; secondly, performing channel filtering on each channel by using a Haar-like template to extract an intermediate layer feature; then, grouping the features of a single channel, randomly selecting a plurality of groups of features to carry out linear weighting combination to form a new candidate feature; secondly, considering the imbalance of sample distribution, proposing an imbalance local Fisher discriminant analysis method to learn a weighting coefficient; and finally, connecting the candidate features of all thechannels in series to form a final feature vector, conducting training in combination with an Adaboost classifier based on a decision tree, and training different detectors for different urinary sediment visible components. According to the urinary sediment detection method, the local information fusion of the urinary sediment tangible image and the imbalance of sample distribution are considered,and the influence of noise is effectively reduced, and the accuracy is high, and the calculation speed is high, and the urinary sediment detection method has very important practical value.

Description

technical field [0001] The invention belongs to the technical field of detection, in particular to a method for detecting urine sediment based on unbalanced Fisher discriminant analysis. Background technique [0002] Urine sediment detection is one of the routine testing items in hospitals, and it plays an important role in the diagnosis and differentiation of kidney diseases, urinary system diseases and infectious diseases. For example, an increase in erythrocytes will indicate urinary tract bleeding, and the location of bleeding can be determined by further examination of the shape of red blood cells; an increase in leukocytes will indicate urinary system infection; increased casts indicate glomerulonephritis, renal tubular and renal dysfunction, etc. Therefore, urine sediment examination is of great significance. [0003] Urinary sediment inspection refers to the examination of the sediment (formed components in urine) after centrifugation with a microscope, which is to ...

AI Technical Summary

Problems solved by technology

[0008] Urine sediment detection method based on image processing, the complexity of microscopic images of urine sediment makes it difficult to obtain an ideal effect in image segmentation, which directly affects the subsequent feature extraction and classification process, resulting in unsatisfactory results of the entire detection process

In addition, the features extracted after image segmentation are too simple, and the feature expression ability is not strong, which is also an important factor affecting the detection performance.

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