Edge finding in magnetic resonance imaging applications: the calculation of the first order derivative of two dimensional images
by Farouk Yahaya
International Journal of Applied Pattern Recognition (IJAPR), Vol. 4, No. 3, 2017

Abstract: 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.

Online publication date: Tue, 12-Sep-2017

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Applied Pattern Recognition (IJAPR):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com