TY - GEN
T1 - Edge detection robust to intensity inhomogeneity
T2 - 21st Iberoamerican Congress on Pattern Recognition, CIARP 2016
AU - Cappabianco, Fàbio A.M.
AU - Lellis, Lucas Santana
AU - Miranda, Paulo
AU - Ide, Jaime S.
AU - Mujica-Parodi, Lilianne R.
N1 - Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
KW - Biomedical imaging
KW - Edge detection
KW - Inhomogeneity
KW - MRI
UR - https://www.scopus.com/pages/publications/85013382011
U2 - 10.1007/978-3-319-52277-7_56
DO - 10.1007/978-3-319-52277-7_56
M3 - Conference contribution
AN - SCOPUS:85013382011
SN - 9783319522760
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 459
EP - 466
BT - Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 21st Iberoamerican Congress, CIARP 2016, Proceedings
A2 - Beltran-Castanon, Cesar
A2 - Famili, Fazel
A2 - Nystrom, Ingela
PB - Springer Verlag
Y2 - 8 November 2016 through 11 November 2016
ER -