Using local texture maps of brain MR images to detect Mild Cognitive Impairment

Rita Lopes Simoes, Cornelis H. Slump, Anne-Marie van Cappellen van Walsum

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

10 Citations (Scopus)
148 Downloads (Pure)


Early detection of Alzheimer's disease is expected to aid in the development and monitoring of more effective treatments. Classification methods have been proposed to distinguish Alzheimer's patients from normal controls using Magnetic Resonance Images. However, their performance drops when classifying patients at a prodromal stage, such as in Mild Cognitive Impairment. Most often, the features used in these classification tasks are related to structural measures such as volume, shape and tissue density. However, microstructural changes have been shown to arise even earlier than these larger-scale alterations. Taking this into account, we propose the use of local statistical texture maps that make no assumptions regarding the location of the affected brain regions. Each voxel contains texture information from its local neighborhood and is used as a feature in the classification of normal controls and Mild Cognitive Impairment patients. The proposed approach obtained an accuracy of 87% (sensitivity 85%, specificity 95%) with Support Vector Machines, outperforming the 63% achieved by the local gray matter density feature.
Original languageUndefined
Title of host publication21st International Conference on Pattern Recognition (ICPR 2012)
Number of pages4
ISBN (Print)978-4-9906441-1-6
Publication statusPublished - Nov 2012
Event21st International Conference on Pattern Recognition 2012 - Tsukuba International Congress Center, Tsukuba Science City, Japan
Duration: 11 Nov 201215 Nov 2012
Conference number: 21

Publication series

PublisherIEEE Computer Society


Conference21st International Conference on Pattern Recognition 2012
Abbreviated titleICPR 2012
CityTsukuba Science City
Internet address


  • EWI-22568
  • METIS-293207
  • IR-83429

Cite this