scholarly journals Computer-assisted Cobb angle measurement from posteroanterior radiographs by a curve fitting method

2016 ◽  
Vol 24 ◽  
pp. 4604-4610 ◽  
Author(s):  
İbrahim YILDIZ
Author(s):  
A Safari ◽  
H Parsaei ◽  
A Zamani ◽  
B Pourabbas

Background: Scoliosis is the most common type of spinal deformity. A universal and standard method for evaluating scoliosis is Cobb angle measurement, but several studies have shown that there is intra- and inter- observer variation in measuring cobb angle manually.Objective: Develop a computer- assisted system to decrease operator-dependent errors in Cobb angle measurement.Methods: The spinal cord in the given x-ray image of the spine is highlighted using contract-stretching technique. The overall structural curvature of the spine is determined by a semi-automatic algorithm aided by the operator. Once the morphologic curve of the spine is determined, in the last step the cobb-angle is estimated by calculating the angle between two normal lines to the spinal curve at the inflection points of the curve.Results: Evaluation results of the developed algorithms using 14 radiographs of patients ( 4 - 40 years old) with cobb angle ranges from 34 - 82 degrees, revealed that the developed algorithm accurately estimated cobb angle. Statistical analysis showed that average angle values estimated using the developed method and that provided by experts are statistically equal. The correlation coefficient between the angle values estimated using the developed algorithm and those provided by the expert is 0.81.Conclusion: Compared with previous algorithms, the developed system is easy to use, less operator-dependent, accurate, and reliable. The obtained results are promising and show that the developed computer-based system could be used to quantify scoliosis by measuring Cobb angle.


2012 ◽  
Vol 19 (2) ◽  
pp. 381-394
Author(s):  
José Pereira ◽  
Octavian Postolache ◽  
Pedro Girão

Using A Segmented Voltage Sweep Mode and A Gaussian Curve Fitting Method to Improve Heavy Metal Measurement System PerformanceThis paper presents a voltammetric segmented voltage sweep mode that can be used to identify and measure heavy metals' concentrations. The proposed sweep mode covers a set of voltage ranges that are centered around the redox potentials of the metals that are under analysis. The heavy metal measurement system can take advantage of the historical database of measurements to identify the metals with higher concentrations in a given geographical area, and perform a segmented sweep around predefined voltage ranges or, alternatively, the system can perform a fast linear voltage sweep to identify the voltammetric current peaks and then perform a segmented voltage sweep around the set of voltages that are associated with the voltammetric current peaks. The paper also includes the presentation of two auto-calibration modes that can be used to improve system's reliability and proposes the usage of a Gaussian curve fitting of voltammetric data to identify heavy metals and to evaluate their concentrations. Several simulation and experimental results, that validate the theoretical expectations, are also presented in the paper.


2010 ◽  
Vol 26 (6-8) ◽  
pp. 801-811 ◽  
Author(s):  
Mingxiao Hu ◽  
Jieqing Feng ◽  
Jianmin Zheng

1993 ◽  
Vol 272 (1) ◽  
pp. 125-134 ◽  
Author(s):  
James M. Jordan ◽  
Michael D. Love ◽  
Harry L. Pardue

2007 ◽  
Vol 46 (13) ◽  
pp. 4549-4560 ◽  
Author(s):  
Q. Peter He ◽  
Jin Wang ◽  
Martin Pottmann ◽  
S. Joe Qin

Author(s):  
Bhavish Sushiel Agarwal ◽  
Jyoti R Desai ◽  
Snehanshu Saha

The use of hand gestures opens a wide range of application for human computer interaction. The paper makes use of haar classifiers and camShift algorithm to track the movement of hand. Parallelism is introduced at every step by segmenting the data from camshaft into an NxN grid. Every block of the grid now represents a lead point which is calculated from mean of all the points belonging to the particular grid. Now we have only N2 points to recognize the curve that was performed by the user in his action. Finally the fit that was found is compared to pre-defined curve fit data to find out the curve using Mahalanobis equation. Parallelism used in reducing the number of points to be fitted allows the recognition to be faster.


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