Automation of an algorithm based on fuzzy clustering for analyzing tumoral heterogeneity in human skin carcinoma tissue sections

This study aims to develop a new FT–IR spectral imaging of tumoral tissue permitting a better characterization of tumor heterogeneity and tumor/surrounding tissue interface. Infrared (IR) data were acquired on 13 biopsies of paraffin-embedded human skin carcinomas. Our approach relies on an innovati...

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Veröffentlicht in:	Laboratory investigation Jg. 91; H. 5; S. 799 - 811
Hauptverfasser:	Sebiskveradze, David, Vrabie, Valeriu, Gobinet, Cyril, Durlach, Anne, Bernard, Philippe, Ly, Elodie, Manfait, Michel, Jeannesson, Pierre, Piot, Olivier
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Elsevier Inc 01.05.2011 Nature Publishing Group US Nature Publishing Group Elsevier (anciennement Nature Publishing Group)
Schlagworte:	631/114/2404 631/67/1813 631/67/2329 Algorithms Automation Biological and medical sciences Biotechnology Cluster Analysis clustering methods Dermatology FT–IR spectroscopy Fundamental and applied biological sciences. Psychology fuzziness index fuzzy C-means Fuzzy Logic Humans Investigative techniques, diagnostic techniques (general aspects) Laboratory Medicine Medical sciences Medicine Medicine & Public Health number of clusters Pathology research-article skin cancers Skin Neoplasms - pathology Spectroscopy, Fourier Transform Infrared Tumors of the skin and soft tissue. Premalignant lesions clustering methods FT–IR spectroscopy number of clusters fuzziness index skin cancers fuzzy C-means Biotechnology Skin disease Methodology Index Decision tree Skin cancer Heterogeneity Tissue Number Clinical biology Human Automation FT-IR spectroscopy Cluster Method Malignant tumor Algorithm Section Infrared spectrometry Skin carcinoma Skin Cancer
ISSN:	0023-6837, 1530-0307, 1530-0307
Online-Zugang:	Volltext
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