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The quality inspection of magnetic resonance imaging (MRI) performance parameters is an important means to ensure the image quality and the reliability of diagnosis results. There are some problems in the manual calculation and eye recognition of the quality inspection parameters, such as strong subjectivity and low efficiency. In view of these facts, an automatic analysis system for MRI quality detection based on QT is proposed and implemented in C++ language. The image processing algorithm is introduced to automatically measure and calculate the quality inspection parameters. The software with comprehensive functions is designed to systematically manage the quality inspection information of MRI. The experimental results show that the automatically calculated parameters are consistent with the manually calculated ones. Accordingly, the accuracy and reliability of the algorithm is verified. The whole system is efficient, convenient and easy to operate, and it can meet the actual needs of MRI quality inspection.
Subject(s)
Algorithms , Image Processing, Computer-Assisted , Magnetic Resonance Imaging , Reference Standards , Reproducibility of ResultsABSTRACT
Objective • To explore the layer segmentation method of optical coherence tomography (OCT) images of retinal vascular diseases using an unsupervised learning method, and compare it with the built-in layering method of OCT machine. Methods • Standardized image acquisition was performed on OCT images from 50 patients with myopic choroidal neovascularization (mCNV) and 20 patients with diabetic macular edema (DME). Standards were established by manual marking of hierarchical information by professional physicians. A retinal multi-layer segmentation method based on the minimization of interlayer energy was proposed, and the results were compared with those obtained by the built-in layering method of OCT machine. The layering accuracy was verified by the unmarked boundary position error. Results • This segmentation method divided the retina of each patient into five layers: internal limiting membrane, lower layer of nerve fiber layer, upper layer of outer nuclear layer, upper layer of ellipsoid zone and Bruch's membrane. The average segmentation error in the overall data set was (4.831±7.015) μm. The error of mCNV group and DME group were (4.839±16.819) μm and (5.048±9.986) μm, respectively, both of which were lower than the automatic measurement results of OCT machine [(13.638±58.024) μm and (14.796±45.342) μm, respectively]. The accuracy of this method at each layer was higher than that of the automatic measurement. Conclusion • This multi-layer segmentation method can be used for segmentation of different types of retinal vascular diseases, and the results are significantly better than those obtained by the built-in method in OCT machine. It can be extended for layer segmentation of other retinal vascular diseases.
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Objective To establish a method of automatic analysis of iodide in drinking water.Methods In accordance with the principles of the National Standard Methods Standard Testing Methods for Dringking Water (GB 5750-2006),the reagent concentration and the ratio of dosage were optimized; the national standard artificial colorimetric detection of iodide in drinking water was substituted by automatic biochemical analyzer colorimetric analysis procedure; the accuracy and the precision of the new method were observed and compared with the National Standard Methods.Results In determination of iodide in drinking water by using automatic biochemical analyzer,the best linear ranges of the standard curve:low concentrations 0-＜ 50 μg/L with a correlation coefficient of 0.999 1,and the detection limit was 0.4 μg/L; at high concentrations of 50-500 μg/L the correlation coefficient was 0.999 3,and the detection limit was 4.0 μg/L.The coefficient of variation was 2.2％-4.2％ and the recovery was between 98.6％-1035％.Compared with the National Standard Methods,the difference was not statistically significant (t =0.97-1.70,all P ＞ 0.05).Conclusions The method of determination of iodide in drinking water by using automatic biochemical analyzer is successfully established.The accuracy and the precision of the method can meet the quality requirements of water quality analysis.
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Objective To evaluate the probability of siblings identification in Identifiler system by using the software of automatic analysis.Methods Using the software of automatic analysis in siblings jdentification.STP genetic typing of 151 pairs of full siblings and 31224 pairs of unrelated individuals from manual simulation were analyzed in 15 STR loci of ldentifiler system.Results Kin probability(W_(FS))of 39.07% full siblings were more than 99.999% while W_(FS) of unrelated individual pairs were 0% .W_(FS) of 60.93% full siblings and 21.3% unrelated individual pairs were all at the range from 99.999% to 1% .W_(FS) of 78.7% unrelated individual pairs 0% full siblings individuals were less than 1% .Therefore,there were notability difference between full siblings and unrelated individual pairs.In addition,testing of 15 STR loci of Identifiler system,it suggested that the pair were siblings when the locus number of the entirely-same is not less than 5 or that of the entirely-different is not more than 1,and that the pair were unrelated individuals when the locus number of the entirely-different is not less than 6 or that of the entirely-8alne is not more than 1.Conclusion The software of automatic analysis and the Identifiler system call be used to siblings identification.
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Objective To investigate the accuracy of computer-assisted interpretation(CAI)in the diagnosis of pediatric critical arrhythmia,and to establish Logistic regression model to evaluate the main risk factors of CAI error.Methods 1147 cases of arrhythmia patients were divided into heart disease(HD)and nonheart disease group(NHD).Candidate risk factors were chosen,from which the vailables statistically significant were entered for logistic regression analysis.Results CAI error rate of HD group and NHD group were 24.9%and 11.4%,showing a significant difference(P＜0.05).Age,underlying diseases,the number of CAI diagnosis entries were the risk factors.Conclusion CAI error pattern was different among HD group and the NHD group.Higher awareness for CAI error risk factors and CAI technology improvement are critical for a lower CAI error rate.
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The stability and accuracy of dosage monitoring system are crucial to guarantee the dosage output veracity and its curative effect,so it is essential to repeatedly inspect dosage monitoring system.But for the present inspection process,some practical problems exist such as comparative complex inspection operation,complicated and time-wasting data analysis and process,etc.This study not only makes some progress on the methods for index inspection and data analysis & process,but also designs a data analysis & process software based on delphi7.0,which makes the whole inspection process simpler and saves much time.
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The local laser solidification treatment among the therapys of age-related macular degeneration, which is immensely important in the field of ophthalmology, has been admited to be of effect. The accurate scope and the times of the cure not only play a important role of convalescence but also have a great influence on eyesight in the laser remedy. The retinal image photographed by a recent technology of ICG(Indocyanine Green) give us easy observation of avascular zone, and let us understand and predict objectively the degree of age-related macular degeneration. In this paper, we introduce the ICGI Analyzer and explain ICG image analysis methods that masure in quantities regions of hyperfluorescein area by choroidal neovascularization and detect transformations of it by image processing techniques. Also, we descript a method that ophthalmologist classify ICGA into several types on the basis of position of CNV(Choroidal NeoVascularization) using this system. We present results of applying ICG retinal image of patients to ICG Analyzer and typical frequency of age-related macular degeneration investigated by experts for testing the objectivity and accuracy of proposed methods.