scholarly journals Developing of a Mathematical Model to Perform Measurements of Axial Vertebral Rotation on Computer-Aided and Automated Diagnosis Systems, Using Raimondi’s Method

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
José Hurtado-Aviles ◽  
Joaquín Roca-González ◽  
Konstantsin Sergeevich Kurochka ◽  
Jose Manuel Sanz-Mengibar ◽  
Fernando Santonja-Medina
Keyword(s):  
Least Squares Method ◽  
Medical Images ◽  
Regression Line ◽  
Mean Value ◽  
Vertebral Rotation ◽  
Automated Diagnosis ◽  
Simplified Approach ◽  
Computer Aided ◽  
Central Column ◽  
Axial Vertebral Rotation

Introduction. Axial vertebral rotation (AVR) is a basic parameter in the study of idiopathic scoliosis and on physical two-dimensional images. Raimondi’s tables are the most used method in the quantification of AVR. The development of computing technologies has enabled the creation of computer-aided or automated diagnosis systems (CADx) with which measurement on medical images can be carried out more quickly, simply, and with less intra and interobserver variabilities than manual methods. Although there are several publications dealing with the measurement of AVR in CADx systems, none of them provides information on the equation or algorithm used for the measurement applying Raimondi’s method. Goal. The aim of this work is to perform a mathematical modelling of the data contained in Raimondi’s tables that enable the Raimondi method to be used in digital medical images more precisely and in a more exact manner. Methods. Data from Raimondi’s tables were tabulated on a first step. After this, each column of Raimondi’s tables containing values corresponding to vertebral body width (D) were adjusted to a curve determined by AVR = f (d). Third, representative values of each rotation divided by D were obtained through the equation of each column D. In a fourth step, a regression line was fitted to the data in each row, and from its equation, the mean value of the D/d distribution is calculated (value corresponding to the central column, D = 45). Finally, a curve was adjusted to the obtained data using the least squares method. Summary and Conclusion. Our mathematical equation allows the Raimondi method to be used in digital images of any format in a more accurate and simplified approach. This equation can be easily and freely implemented in any CADx system to quantify AVR, providing a more precise use of Raimondi’s method, as well as being used in traditional manual measurement as it is performed with Raimondi tables.

APPLICATION OF ENTROPIES FOR AUTOMATED DIAGNOSIS OF ABNORMALITIES IN ULTRASOUND IMAGES: A REVIEW

2017 ◽  
Vol 17 (07) ◽  
pp. 1740012 ◽  
Author(s):  
YUKI HAGIWARA ◽  
VIDYA K SUDARSHAN ◽  
SOOK SAM LEONG ◽  
ANUSHYA VIJAYNANTHAN ◽  
KWAN HOONG NG
Keyword(s):  
Medical Image ◽  
Biomedical Applications ◽  
Medical Images ◽  
Medical Image Analysis ◽  
Ultrasound Images ◽  
Automated Diagnosis ◽  
Computer Aided ◽  
Automated Algorithms ◽  
Aided Diagnosis ◽  
System Entropy

Automation of diagnosis process in medical imaging using various computer-aided techniques is a leading topic of research. Among many computer-aided methods, nonlinear entropies are widely applied in the development of automated algorithms to diagnose abnormalities present in medical images. The use of entropy features in development of Computer-Aided Diagnosis (CAD) may enhance the accuracy of the system. Entropy features depict the nonlinearity of images and thereby the presence of complexity in the images. Various types of entropies have been employed in medical image analysis for automated diagnosis of abnormalities present in the images. This paper focuses on the diverse types of entropies employed in the development of CAD systems for the diagnosis of abnormalities in the medical images. In addition to the diagnosis, these entropies can be used to differentiate the images based on the severity of the abnormalities and for other biomedical applications.

Simplified approach to confidence limited in radioimmunassay.

1980 ◽  
Vol 26 (6) ◽  
pp. 763-765 ◽  
Author(s):  
R C Baxter
Keyword(s):  
Quality Control ◽  
Mean Value ◽  
Confidence Limits ◽  
Simple Method ◽  
Response Variable ◽  
Simplified Approach ◽  
Quadratic Curve ◽  
Wide Range ◽  
Advanced Computing ◽  
Routine Quality Control

Abstract A simple method of calculating confidence limits for radioimmunoassay data is presented. The method involves the use of the within-assay variation in dose estimate of three routine quality-control specimens, measured in repeated assays, to estimate the confidence limits for results on unknown samples. Results for control specimens are combined by calculating the unique quadratic curve fitting a graph of within-assay standard deviation vs mean value for each control. This method requires no special data accumulation or advanced computing equipment. For cortisol, lutropin, and thyroxine radioimmunoassays, confidence limits calculated in this way have been compared with those calculated from the variance of the response variable “B/B0” in repeated standard curves. Both methods agree well with actual limits observed when plasma pools containing a wide range of hormone concentrations are assayed repeatedly.

Incorrect least-squares regression coefficients in method-comparison analysis.

1979 ◽  
Vol 25 (3) ◽  
pp. 432-438 ◽  
Author(s):  
P J Cornbleet ◽  
N Gochman
Keyword(s):  
Standard Deviation ◽  
Least Squares ◽  
Least Squares Method ◽  
Regression Line ◽  
Method Comparison ◽  
Regression Coefficients ◽  
Least Squares Regression ◽  
Data Set ◽  
Data Points ◽  
Regression Slopes

Abstract The least-squares method is frequently used to calculate the slope and intercept of the best line through a set of data points. However, least-squares regression slopes and intercepts may be incorrect if the underlying assumptions of the least-squares model are not met. Two factors in particular that may result in incorrect least-squares regression coefficients are: (a) imprecision in the measurement of the independent (x-axis) variable and (b) inclusion of outliers in the data analysis. We compared the methods of Deming, Mandel, and Bartlett in estimating the known slope of a regression line when the independent variable is measured with imprecision, and found the method of Deming to be the most useful. Significant error in the least-squares slope estimation occurs when the ratio of the standard deviation of measurement of a single x value to the standard deviation of the x-data set exceeds 0.2. Errors in the least-squares coefficients attributable to outliers can be avoided by eliminating data points whose vertical distance from the regression line exceed four times the standard error the estimate.

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