TY - GEN
T1 - Dual-space pyramid matching for medical image classification
AU - Hu, Yang
AU - Li, Mingjing
AU - Li, Zhiwei
AU - Ma, Wei-Ying
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2007
Y1 - 2007
N2 - With the increasing of medical images that are routinely acquired in clinical practice, automatic medical image classification has become an important research topic recently. In this paper, we propose an efficient medical image classification algorithm, which works by mapping local image patches to multi-resolution histograms built both in feature space and image space and then matching sets of features though weighted histogram intersection. The matching produces a kernel function that satisfies Mercer's condition, and a multi-class SVM classifier is then applied to classify the images. The dual-space pyramid matching scheme explores not only the distribution of local features in feature space but also their spatial layout in the images. Therefore, more accurate implicit correspondence is built between feature sets. We evaluate the proposed algorithm on the dataset for the automatic medical image annotation task of ImageCLEFmed 2005. It outperforms the best result of the campaign as well as the pyramid matchings that only perform in single space. © Springer-Verlag Berlin Heidelberg 2007.
AB - With the increasing of medical images that are routinely acquired in clinical practice, automatic medical image classification has become an important research topic recently. In this paper, we propose an efficient medical image classification algorithm, which works by mapping local image patches to multi-resolution histograms built both in feature space and image space and then matching sets of features though weighted histogram intersection. The matching produces a kernel function that satisfies Mercer's condition, and a multi-class SVM classifier is then applied to classify the images. The dual-space pyramid matching scheme explores not only the distribution of local features in feature space but also their spatial layout in the images. Therefore, more accurate implicit correspondence is built between feature sets. We evaluate the proposed algorithm on the dataset for the automatic medical image annotation task of ImageCLEFmed 2005. It outperforms the best result of the campaign as well as the pyramid matchings that only perform in single space. © Springer-Verlag Berlin Heidelberg 2007.
UR - https://www.scopus.com/pages/publications/84886433698
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84886433698&origin=recordpage
U2 - 10.1007/978-3-540-69423-6_10
DO - 10.1007/978-3-540-69423-6_10
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9783540694212
VL - 4351 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 96
EP - 105
BT - Advances in Multimedia Modeling - 13th International Multimedia Modeling Conference, MMM 2007, Proceedings
T2 - 13th International Multimedia Modeling Conference, MMM 2007
Y2 - 9 January 2007 through 12 January 2007
ER -