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Information et communication   > Accueil   > Des données à la décision   > Numéro 1   > Article

Données image et décision : détection automatique de variations dans des séries temporelles par réseau de Kohonen

Using the quantization error from Self‐Organizing Map (SOM) output for fast detection of critical variations in image time series


Birgitta Dresp-Langley
ICube Lab CNRS and University of Strasbourg - Strasbourg
France

John Mwangi Wandeto
ICube Lab CNRS and University of Strasbourg - Strasbourg
France

Henry Okola Nyongesa
Dedan Kimathi University of Technology - Nyeri
Kenya



Publié le 15 janvier 2018   DOI :

Résumé

Abstract

Mots-clés

Keywords

The quantization error (QE) from Self-Organizing Map (SOM) output after learning is exploited in these
studies. SOM learning is applied on time series of spatial contrast images with variable relative amount of white and dark
pixel contents, as in monochromatic medical images or satellite images. It is proven that the QE from the SOM output
after learning provides a reliable indicator of potentially critical changes in images across time. The QE increases linearly
with the variability in spatial contrast contents of images across time when contrast intensity is kept constant. The hitherto
unsuspected capacity of this metric to capture even the smallest changes in large bodies of image time series after using
ultra-fast SOM learning is illustrated on examples from SOM learning studies on computer generated images, MRI image
time series, and satellite image time series. Linear trend analysis of the changes in QE as a function of the time an image
of a given series was taken gives proof of the statistical reliability of this metric as an indicator of local change. It is shown
that the QE is correlated with significant clinical, demographic, and environmental data from the same reference time
period during which test image series were recorded. The findings show that the QE from SOM, which is easily
implemented and requires computation times no longer than a few minutes for a given image series of 20 to 25, is useful
for a fast analysis of whole series of image data when the goal is to provide an instant statistical decision relative to
change/no change between images.

The quantization error (QE) from Self-Organizing Map (SOM) output after learning is exploited in these
studies. SOM learning is applied on time series of spatial contrast images with variable relative amount of white and dark
pixel contents, as in monochromatic medical images or satellite images. It is proven that the QE from the SOM output
after learning provides a reliable indicator of potentially critical changes in images across time. The QE increases linearly
with the variability in spatial contrast contents of images across time when contrast intensity is kept constant. The hitherto
unsuspected capacity of this metric to capture even the smallest changes in large bodies of image time series after using
ultra-fast SOM learning is illustrated on examples from SOM learning studies on computer generated images, MRI image
time series, and satellite image time series. Linear trend analysis of the changes in QE as a function of the time an image
of a given series was taken gives proof of the statistical reliability of this metric as an indicator of local change. It is shown
that the QE is correlated with significant clinical, demographic, and environmental data from the same reference time
period during which test image series were recorded. The findings show that the QE from SOM, which is easily
implemented and requires computation times no longer than a few minutes for a given image series of 20 to 25, is useful
for a fast analysis of whole series of image data when the goal is to provide an instant statistical decision relative to
change/no change between images.

Self-Organizing Map (SOM) quantization error image time series spatial contrast variability change detection

Self-Organizing Map (SOM) quantization error image time series spatial contrast variability change detection