Inter-cluster features for medical image classification

Siyamalan Manivannan, Ruixuan Wang, Emanuele Trucco

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

Feature encoding plays an important role for medical image classification. Intra-cluster features such as bag of visual words have been widely used for feature encoding, which are based on the statistical information within each clusters of local features and therefore fail to capture the inter-cluster statistics, such as how the visual words co-occur in images. This paper proposes a new method to choose a subset of cluster pairs based on the idea of Latent Semantic Analysis (LSA) and proposes a new inter-cluster statistics which capture richer information than the traditional co-occurrence information. Since the cluster pairs are selected based on image patches rather than the whole images, the final representation also captures the local structures present in images. Experiments on medical datasets show that explicitly encoding inter-cluster statistics in addition to intra-cluster statistics significantly improves the classification performance, and adding the rich inter-cluster statistics performs better than the frequency based inter-cluster statistics.
Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention – MICCAI 2014
Subtitle of host publication17th International Conference, Boston, MA, USA, September 14-18, 2014, Proceedings, Part III
EditorsPolina Golland, Nobuhiko Hata, Christian Barillot, Joachim Hornegger, Robert Howe
PublisherSpringer International Publishing
Pages345-352
Number of pages8
ISBN (Electronic)9783319104430
ISBN (Print)9783319104423
DOIs
Publication statusPublished - 2014
Event17th International Conference on Medical Image Computing and Computer-Assisted Intervention - Boston, MA., United States
Duration: 14 Sep 201418 Sep 2014
http://miccai2014.org/index.html

Publication series

NameLecture notes in computer science
PublisherSpringer International Publishing
Volume8675
ISSN (Print)0302-9743

Conference

Conference17th International Conference on Medical Image Computing and Computer-Assisted Intervention
Abbreviated titleMICCAI 2014
CountryUnited States
CityBoston, MA.
Period14/09/1418/09/14
Internet address

Fingerprint Dive into the research topics of 'Inter-cluster features for medical image classification'. Together they form a unique fingerprint.

  • Cite this

    Manivannan, S., Wang, R., & Trucco, E. (2014). Inter-cluster features for medical image classification. In P. Golland, N. Hata, C. Barillot, J. Hornegger, & R. Howe (Eds.), Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014: 17th International Conference, Boston, MA, USA, September 14-18, 2014, Proceedings, Part III (pp. 345-352). (Lecture notes in computer science; Vol. 8675). Springer International Publishing. https://doi.org/10.1007/978-3-319-10443-0_44