Development of a memetic clustering algorithm for optimal spectral histology: application to FTIR images of normal human colon.
Fiche publication
Date publication
mai 2016
Journal
The Analyst
Auteurs
Membres identifiés du Cancéropôle Est :
Dr GOBINET Cyril, Dr GUENOT Dominique
Tous les auteurs :
Farah I, Nguyen TN, Groh A, Guenot D, Jeannesson P, Gobinet C
Lien Pubmed
Résumé
The coupling between Fourier-transform infrared (FTIR) imaging and unsupervised classification is effective in revealing the different structures of human tissues based on their specific biomolecular IR signatures; thus the spectral histology of the studied samples is achieved. However, the most widely applied clustering methods in spectral histology are local search algorithms, which converge to a local optimum, depending on initialization. Multiple runs of the techniques estimate multiple different solutions. Here, we propose a memetic algorithm, based on a genetic algorithm and a k-means clustering refinement, to perform optimal clustering. In addition, this approach was applied to the acquired FTIR images of normal human colon tissues originating from five patients. The results show the efficiency of the proposed memetic algorithm to achieve the optimal spectral histology of these samples, contrary to k-means.
Mots clés
Algorithms, Cluster Analysis, Colon, diagnostic imaging, Humans, Spectroscopy, Fourier Transform Infrared
Référence
Analyst. 2016 May;141(11):3296-304