Edge finding in magnetic resonance imaging applications: the calculation of the first order derivative of two dimensional images
Farouk Yahaya · International Journal of Applied Pattern Recognition · 2017
This paper proposes a novel edge detection algorithm based on model fitting and the calculation of the first order derivative of two-dimensional images. It is central to mention that the aforementioned calculation can only be done through approximations since the image being used is made of a sequel of discrete samples, and the continuity is brought in through the model function which needs to offer the property of first order differentiability. The cubic bivariate polynomial was used in this paper. Indeed, the partial first order derivatives of the images are calculated after fitting the model function to the image data. It is found that the cubic bivariate polynomial shows visible edges of the images as seen in the results section. The usefulness of the methodology is also tested on a range of images, practical applications, and performance analysis under realistic conditions.