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Open Access Research article

Similarity maps and hierarchical clustering for annotating FT-IR spectral images

Qiaoyong Zhong1, Chen Yang1, Frederik Großerüschkamp2, Angela Kallenbach-Thieltges2, Peter Serocka12, Klaus Gerwert12 and Axel Mosig2*

Author Affiliations

1 Department of Biophysics, CAS-MPG Partner Institute and Key Laboratory for Computational Biology, 320 Yueyang Road, 200031 Shanghai, China

2 Department of Biophysics, Ruhr University Bochum, Universitätsstraße 150, 44801 Bochum, Germany

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BMC Bioinformatics 2013, 14:333  doi:10.1186/1471-2105-14-333

Published: 20 November 2013



Unsupervised segmentation of multi-spectral images plays an important role in annotating infrared microscopic images and is an essential step in label-free spectral histopathology. In this context, diverse clustering approaches have been utilized and evaluated in order to achieve segmentations of Fourier Transform Infrared (FT-IR) microscopic images that agree with histopathological characterization.


We introduce so-called interactive similarity maps as an alternative annotation strategy for annotating infrared microscopic images. We demonstrate that segmentations obtained from interactive similarity maps lead to similarly accurate segmentations as segmentations obtained from conventionally used hierarchical clustering approaches. In order to perform this comparison on quantitative grounds, we provide a scheme that allows to identify non-horizontal cuts in dendrograms. This yields a validation scheme for hierarchical clustering approaches commonly used in infrared microscopy.


We demonstrate that interactive similarity maps may identify more accurate segmentations than hierarchical clustering based approaches, and thus are a viable and due to their interactive nature attractive alternative to hierarchical clustering. Our validation scheme furthermore shows that performance of hierarchical two-means is comparable to the traditionally used Ward’s clustering. As the former is much more efficient in time and memory, our results suggest another less resource demanding alternative for annotating large spectral images.

Hierarchical clustering; Cluster validation; FT-IR microscopy; Raman microscopy; Image analysis; Similarity maps