ABSTRACT
Objective:To provide consistent data basis for the application of reference intervals for children blood cell analysis in different testing systems.Methods:According to the requirements of American Institute for Clinical and Laboratory Standardization (CLSI) EP9-A3 document, 45 samples were collected and Sysmex XN20-A1 were used as reference system. Beckman DxH800, Siemens ADVIA 2120i, and Mindray BC5310 were comparison systems. Complete blood count and leukocyte classification were performed by four systems. The outliers of the detection results were tested by the generalized extreme student deviate (ESD) method. An optimal regression model was selected by scatter diagram, deviation diagram and frequency distribution diagram, which was used to fit the regression equation and calculate the deviation at the medical decision level and reference interval. The acceptable range for blood count deviation was cited from the Analytical Quality Specifications for Routine Tests in Clinical Hematology. The acceptable range for leukocyte classification was based on the EQA program of Royal College of Pathologists of Australasia (RCPA).Results:After the outliers were deleted, the scatter plot showed a linear relationship between the reference system and the three comparison systems. The deviation plot showed that the differences were variable. Deming regression or Passing-Bablok regression was selected according to the data distribution. The determination coefficient R2 of reference system and three comparison systems ranged from 0.95 to 0.99 in blood count and leukocyte classification. At the upper and lower limits of the reference interval, the deviations between XN-20A1 and ADVIA 2120 system were all acceptable, except for MONO# at 0.12×10 9/L. The deviations of all parameters at medical decision level were within acceptable ranges. The lower limit of PLT is partially unacceptable at the level of medical decision related to treatment and prognosis. Conclusions:The results of complete blood count and leukocyte classification in reference system and the comparison system had good consistency within the children′s reference interval. Our study provided a scientific basis for the feasibility of adopting a unified reference interval for different detection systems.
ABSTRACT
A new leukocyte classification method for recognition of five types of human peripheral blood smear based on mean-shift clustering is proposed. The key idea of the proposed method is to extract the texture features of leukocytes in a visual manner which can benefit from human eyes. Firstly, some feature points are extracted in a gray leukocyte image by mean-shift. Secondly, these feature points are used as seeds of the region growing to expand feature regions which can express texture in visual mode to a certain extent. Finally, a parameter vector of these regions is extracted as the texture feature. Combing the vector with the geometric features of the leukocyte, the five typical classes of leukocytes can be recognized successfully using artificial neural network (ANN). A total number of 1 310 leukocyte images have been tested and the accurate rate of recognition for neutrophil, eosinophil, basophil, lymphocyte and monocyte are 95.4%, 93.8%, 100%, 93.1% and 92.4%, respectively, which shows the feasibility and high robustness of the proposed method.
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Objective To investigate the clinical features and value of white blood cell(WBC) count during influenza diagnosis.Methods Compare with leukocyte count and its classification,clinical features between 38 cases of influenza A and 55 cases of influenza B patients.Results The follow results have were significant difference between these two group(P<0.05):The WBC content (WBC),neutrophil count (NEUT),lymphocyte(LYMPH) count and platelet (PLT).In both group,WBC were mostly in normal range.However,WBC and NEUT% in the group A were 15.79% and 34.21%,higher than the B group 7.27%,25.45%.Additionaly,WBC and NEUT% in upper respiratory tract infection group were 59.26% and 66.67%,higher than the normal ranger,and group influenza A flow patients with higher proportion for headache and crackles,were 47.36% and 52.63%,respectively(P<0.05).Conclusion The WBC count and its classification have significant difference between influenza A and B.Furthermore,patients with influenza A virus are more likely to have co-infection with bacteria.
ABSTRACT
Objective:To design a automatic classification system for leukocytes in order to increase detection speed of manual microscope inspection and reduce the inaccurate detected results in clinical laboratory; and to evaluate this system.Methods: In this system, the image processing and algorithm classifying were achieved by MATLAB software consisted of digital image processing module and automatic classification module. Classification decision for discrimination function and simulated detection for this system were achieved by using automatic classification module.Results: In the simulation experiments, the detection results for sample cell demonstrated the recognition accuracy can achieve to 93% and the speed can achieve to 97.8 cells per second for this system.Conclusion: The automatic recognition and classification system for leukocyte not only reduces human consumption, but also improves the detection accuracy and detection speed for leukocyte, and it has some significant in clinical application.