Thickness NETwork (ThickNet) features for the detection of prodromal AD

Pradeep Reddy Raamana*, Lei Wang, Mirza Faisal Beg

*Corresponding author for this work

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

1 Scopus citations

Abstract

Regional analysis of cortical thickness has been studied extensively in building imaging biomarkers for early detection of Alzheimer's disease (AD), but not its inter-regional covariation. We present novel features based on the inter-regional co-variation of cortical thickness. Initially the cortical labels of each patient is partitioned into small patches (graph nodes) by spatial k-means clustering. A graph is then constructed by establishing a link between two nodes if the difference in thickness between the nodes is below a certain threshold. From this binary graph, thickness network (ThickNet) features are computed using nodal degree, betweenness and clustering coefficient measures. Fusing them with multiple kernel learning, we demonstrate their potential for the detection of prodromal AD.

Original languageEnglish (US)
Title of host publicationMachine Learning in Medical Imaging - 4th International Workshop, MLMI 2013, Held in Conjunction with MICCAI 2013, Proceedings
PublisherSpringer Verlag
Pages114-122
Number of pages9
ISBN (Print)9783319022666
DOIs
StatePublished - Jan 1 2013
Event4th International Workshop on Machine Learning in Medical Imaging, MLMI 2013, Held in Conjunction with 16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013 - Nagoya, Japan
Duration: Sep 22 2013Sep 22 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8184 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other4th International Workshop on Machine Learning in Medical Imaging, MLMI 2013, Held in Conjunction with 16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013
CountryJapan
CityNagoya
Period9/22/139/22/13

Keywords

  • alzheimer
  • fusion
  • mild cognitive impairment
  • network
  • thickness

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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