, , , , , ,

Abstract

Many geometrical angles are measured directly on bone radiographs and are difficult to recall, we wanted to explore an automatic method of measurement. Edge detection was needed to determine bone edges and use them for calculation. There is no consensus on which is the best one for use in skeletal radiographs. We decided to compare commonly used edge detection methods qualitatively and quantitatively for measuring the carrying angle of the elbow using a framework we developed in PHP: Hypertext Preprocessor. Five-Hundred patients’ elbow radiographs were collected. They were run through the measurement algorithm using the following edge detection methods: Sobel, Scharr, Prewitt, Frei-Chen, Kirsch, Robinson, Difference of Gaussians (DoG), Laplacian of Gaussian (LoG), Canny, Hough. Five observers manually measured the carrying angle. Results were compared using Intraclass Correlation Coefficient (ICC), Regression Analysis and Validity calculation. The Robinson algorithm was best in the qualitative analysis. Observer ICC was 0.643 which showed a strong agreement. Quantitative analysis revealed that, developing bone caused a significant bias compared to mature bone and DoG algorithm was the best due to low bias, high validity and low processing time. Automated radiographic measurement of the carrying angle of the elbow is a feasible and reliable process.