TY - GEN
T1 - Medical image segmentation using improved affinity propagation
AU - Zhu, Hong
AU - Xu, Jinhui
AU - Hu, Junfeng
AU - Chen, Jing
N1 - Publisher Copyright: © Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - Affinity Propagation (AP) is an effective clustering method with a number of advantages over the commonly used k-means clustering. For example, it does not need to specify the number of clusters in advance, and can handle clusters with general topology, which makes it uniquely suitable for medical image segmentation as most of the objects in medical images are not roundly shaped. One factor hampering its applications is its relatively slow speed, especially for large-size images. To overcome this difficulty, we propose in this paper an Improved Affinity Propagation (IMAP) method with several improved features. Particularly, our IMAP method can adaptively select the key parameter p in AP according to the medical image gray histogram, and thus can greatly speed up convergence. Experimental results suggest that IMAP has a higher image entropy, lower class square error contrast, and shorter runtime than the AP algorithm.
AB - Affinity Propagation (AP) is an effective clustering method with a number of advantages over the commonly used k-means clustering. For example, it does not need to specify the number of clusters in advance, and can handle clusters with general topology, which makes it uniquely suitable for medical image segmentation as most of the objects in medical images are not roundly shaped. One factor hampering its applications is its relatively slow speed, especially for large-size images. To overcome this difficulty, we propose in this paper an Improved Affinity Propagation (IMAP) method with several improved features. Particularly, our IMAP method can adaptively select the key parameter p in AP according to the medical image gray histogram, and thus can greatly speed up convergence. Experimental results suggest that IMAP has a higher image entropy, lower class square error contrast, and shorter runtime than the AP algorithm.
KW - Affinity propagation
KW - Gray level histogram
KW - Medical image segmentation
UR - https://www.scopus.com/pages/publications/85015883155
U2 - 10.1007/978-3-319-54609-4_15
DO - 10.1007/978-3-319-54609-4_15
M3 - Conference contribution
SN - 9783319546087
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 208
EP - 215
BT - Computational Modeling of Objects Presented in Images
A2 - Barneva, Reneta P.
A2 - Tavares, Joao Manuel R.S.
A2 - Brimkov, Valentin E.
PB - Springer Verlag
T2 - 5th International Symposium on Computational Modeling of Objects Represented in Images: Fundamentals, Methods and Applications, CompIMAGE 2016
Y2 - 21 September 2016 through 23 September 2016
ER -