ACF-based urine sediment detection method
A detection method and urine sediment technology, applied in the field of detection, can solve the problems that image segmentation cannot achieve good results, the recognition rate of red blood cells and white blood cells is not high, and the background and target area cannot be segmented, etc., and achieve excellent anti-noise characteristics and detection effects. Good, low false detection rate effect
A detection method and urine sediment technology, applied in the field of detection, can solve the problems that image segmentation cannot achieve good results, the recognition rate of red blood cells and white blood cells is not high, and the background and target area cannot be segmented, etc., and achieve excellent anti-noise characteristics and detection effects. Good, low false detection rate effect
CN108414734AInactive Publication Date: 2018-08-17SOUTHEAST UNIV
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[0031] The technical solution of the present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.
[0032] A urine sediment detection method based on ACF, such as figure 1As shown, different detectors are trained for different urinary sediment formed components, and the training methods for different urinary sediment formed components are the same, with different parameters, including training and detection phases. The method of the training phase is to first perform channel calculation on the training samples, including LUV, gradient magnitude and gradient direction histogram in six directions, and then perform pooling operations on ten channels, and the data of ten channels are vectorized end-to-end in sequence to obtain The feature vector is used to train the classifier using the soft-cascaded adaboost algorithm. The final model has 2048 weak classifiers, and each weak classifier is a decision tree with a depth ...
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The invention discloses an ACF-based urine sediment detection method. Different detectors are trained for different urine sediment visible components. The method includes the following steps: (1) training: performing path calculation on a training sample, including LUV, gradient amplitude and the gradient direction histogram in six directions, then pooling ten paths, vectorizing the ten paths to obtain a feature vector, and training classifiers by using a soft cascaded adaboost algorithm, wherein each weak classifier is a decision making tree with the depth of 2; and (2) testing: extracting ten path features from a sampling window by using a sliding window detection technology, vectorizing the ten path features to form feature vectors, improving the detection speed by using a fast featurepyramid, performing test by using a trained model, and judging whether the sample belongs to the urine sediment visible components or not. The method has the advantages of good utilization of the feature information of the urine sediment visible components, effectiveness in reducing the influences of noises, high accuracy, fast calculation speed and great practical values.
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technical field [0001] The invention relates to the technical field of detection, in particular to an ACF-based urine sediment detection method. 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 detect and count the formed components in ...
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Patent Timeline
17 Aug 2018
Publication
CN108414734A
- IPC
- G01N33/493; G06T7/90
- CPC
- G01N33/493; G06T7/90; G06T2207/10024; G06T2207/20081; G06T2207/30024
- Inventors
- 杨万扣; 孙启明