TY - CHAP
T1 - Semi-automatic Segmentation of the Prostate
AU - Zwiggelaar, Reyer
AU - Zhu, Yanong
AU - Williams, Stuart
PY - 2003/5/22
Y1 - 2003/5/22
N2 - A semi-automatic method has been developed which segments the prostate in slices of Magnetic Resonance Imaging (MRI) data. The developed approach exploits the characteristics of the anatomical shape of the prostate when represented in a polar transform space. Simple techniques, such as line detection and non-maximum suppression, are used to track the boundary of the prostate. The initial results, based on a small set of data, indicate a good correlation with expert based manual segmentation.
AB - A semi-automatic method has been developed which segments the prostate in slices of Magnetic Resonance Imaging (MRI) data. The developed approach exploits the characteristics of the anatomical shape of the prostate when represented in a polar transform space. Simple techniques, such as line detection and non-maximum suppression, are used to track the boundary of the prostate. The initial results, based on a small set of data, indicate a good correlation with expert based manual segmentation.
UR - http://www.scopus.com/inward/record.url?scp=31744442732&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-44871-6_128
DO - 10.1007/978-3-540-44871-6_128
M3 - Chapter
SN - 3540402179
SN - 9783540402176
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 1108
EP - 1116
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
A2 - Perales, Francisco Jose
A2 - Campilho, Aurelio J. C.
A2 - Perez, Nicolas Perez
A2 - Perez, Nicolas Perez
PB - Springer Nature
ER -