Detection of Bone Fracture using Prewitt Edge Algorithm and Comparing with Laplacian Algorithm to Increase Accuracy and Sensitivity.

2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC) Pub Date : 2023-07-06 DOI:10.1109/ICESC57686.2023.10193548

N. Nalini, G. Uganya, M. Sathesh, M. Sheela

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Abstract

The purpose of the research was to is to compare accuracy and specificity in the bone fracture detection using novel modified Prewitt Edge Detection (PED) with Laplacian Edge Detection (LED). Two groups are compared, novel modified Prewitt Edge Detection (PED) (N=10) and Laplacian edge detection (LED) (N=10) The overall sample size was calculated using the G Power software with an alpha of 0.05, enrollment ratio of 0.1, confidence interval of 5%, and power of 80%. Using the SPSS statistical package, an independent sample t-test was used to compare the accuracy and specificity rate. Novel modified Prewitt edge detection (PED) algorithm found to be statistically significant when compared with the Laplacian edge detection (LED) classifier which gives accuracy p= 0.026, and specificity p=0.001(p<0.05) of bone fracture X-ray image. The Laplacian edge detection approach seems to be outperformed by a new modified Prewitt edge detection algorithm.

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用Prewitt边缘算法检测骨折，并与拉普拉斯算法进行比较，提高准确性和灵敏度。

本研究的目的是比较新型改进的Prewitt边缘检测(PED)和拉普拉斯边缘检测(LED)在骨折检测中的准确性和特异性。比较两组，新型改进Prewitt边缘检测(PED) (N=10)和拉普拉斯边缘检测(LED) (N=10)，总体样本量采用G Power软件计算，alpha为0.05，入组比为0.1，置信区间为5%，功率为80%。采用SPSS统计软件包，采用独立样本t检验比较准确率和特异性。与拉普拉斯边缘检测(LED)分类器相比，新型改进Prewitt边缘检测(PED)算法对骨折x线图像的准确率p= 0.026，特异性p=0.001(p<0.05)，具有统计学意义。拉普拉斯边缘检测方法似乎被一种新的改进的Prewitt边缘检测算法所优于。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)

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