Development of a memetic clustering algorithm for optimal spectral histology: application to FTIR images of normal human colon. Issue 11 (25th April 2016)

• Record Type: Journal Article
• Authors: Farah, Ihsen; Nguyen, Thi Nguyet Que; Groh, Audrey; Guenot, Dominique; Jeannesson, Pierre; Gobinet, Cyril
• Is Part Of: Analyst. Volume 141:Issue 11(2016)
• Page Start: 3296
• Page End: 3304
• Publication Date: 2016-04-25
• Subjects: Chemistry, Analytic -- Periodicals; 543
• DOI: 10.1039/c5an02227d
• Languages: English
• ISSNs: 0003-2654

Abstract

Abstract : A memetic clustering algorithm combining a genetic algorithm with k -means clustering for an optimal spectral histology of a normal human colon. Abstract : 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.