Please use this identifier to cite or link to this item:
|Title:||Edge Detection Robust to Intensity Inhomogeneity: A 7T MRI Case Study|
|Authors:||Cappabianco, Fabio A. M. [UNIFESP]|
Lellis, Lucas Santana [UNIFESP]
Ide, Jaime S.
Mujica-Parodi, Lilianne R.
|Publisher:||Springer International Publishing Ag|
|Citation:||Progress In Pattern Recognition, Image Analysis, Computer Vision, And Applications, Ciarp 2016. Cham, v. 10125, p. 459-466, 2017.|
|Abstract:||Edge detection is a fundamental operation for computer vision and image processing applications. As of 1986, John Canny proposed a methodology that became known due to its simplicity, small number of parameters, and high accuracy. The method was designed to optimally detect, locate, and trace single edges over each local gradient maximum. Since then, a number of works were proposed but none of these improvements were capable of dealing with non-uniform intensity, which are notably present in ultra high field magnetic resonance imaging (MRI). In this paper, we evaluate the effects of inhomogeneity correction over automatic edge detection methods over 7T MRI. Importantly, we propose a non-supervised edge detection method which improves the accuracy of state of the art in 28.0% as detecting head and brain edges.|
|Appears in Collections:||Trabalho apresentado em evento|
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.