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

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.