A novel fuzzy clustering algorithm for the analysis of axillary lymph node tissue sections |
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Authors: | Xiao-Ying Wang Jonathan M Garibaldi Benjamin Bird Michael W George |
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Affiliation: | (1) School of Computer Science and Information Technology, The University of Nottingham, Nottingham, UK;(2) School of Chemistry, The University of Nottingham, Nottingham, UK |
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Abstract: | Recently Fourier Transform Infrared (FTIR) spectroscopic imaging has been used as a tool to detect the changes in cellular
composition that may reflect the onset of a disease. This approach has been investigated as a mean of monitoring the change
of the biochemical composition of cells and providing a diagnostic tool for various human cancers and other diseases. The
discrimination between different types of tissue based upon spectroscopic data is often achieved using various multivariate
clustering techniques. However, the number of clusters is a common unknown feature for the clustering methods, such as hierarchical
cluster analysis, k-means and fuzzy c-means. In this study, we apply a FCM based clustering algorithm to obtain the best number
of clusters as given by the minimum validity index value. This often results in an excessive number of clusters being created
due to the complexity of this biochemical system. A novel method to automatically merge clusters was developed to try to address
this problem. Three lymph node tissue sections were examined to evaluate our new method. These results showed that this approach
can merge the clusters which have similar biochemistry. Consequently, the overall algorithm automatically identifies clusters
that accurately match the main tissue types that are independently determined by the clinician. |
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Keywords: | FTIR imaging Fuzzy c-means Clustering Automated merge clusters Lymph node |
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