Using spatial characteristics to aid automation of SOM segmentation of functional image data

Robert Grossman, Erzsébet Merényi, Patrick O’Driscoll

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

2 Scopus citations

Abstract

We propose a new similarity measure, Combined Connectivity and Spatial Adjacency (CCSA), to be used in hierarchical agglomerative clustering (HAC) for automated segmentation of Self-Organizing Maps (SOMs, Kohonen [1]). The CCSA measure is specifically designed to assist segmentation of large, complex, functional image data by exploiting general spatial characteristics of such data. The proposed CCSA measure is constructed from two strong indicators of cluster structure: the degree of localization of data points in physical space and the degree of connectivity of SOM prototypes (as defined by Tasdemir and Merényi [2]). The new measure is expected to enhance cluster capture in large functional image data cubes such as hyperspectral imagery or fMRI brain images, where many relevant clusters exist with widely varying statistical properties and in complex relationships both in feature space and in physical (image) space. We demonstrate the effectiveness of our approach using the CCSA measure on progressively complex synthetic spatial data and on real fMRI brain data.

Original languageEnglish (US)
Title of host publication12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization, WSOM 2017 - Proceedings
EditorsJean-Charles Lamirel, Madalina Olteanu, Marie Cottrell
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509066384
DOIs
StatePublished - Aug 29 2017
Event12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization, WSOM 2017 - Nancy, France
Duration: Jun 28 2017Jun 30 2017

Publication series

Name12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization, WSOM 2017 - Proceedings

Other

Other12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization, WSOM 2017
CountryFrance
CityNancy
Period6/28/176/30/17

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics

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