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

BMC Bioinformatics

Qiaoyong Zhong²  Chen Yang²  Frederik Großerüschkamp¹  Angela Kallenbach-Thieltges¹  Peter Serocka¹  Klaus Gerwert¹  Axel Mosig¹ 

[1] Department of Biophysics, Ruhr University Bochum, Universitätsstraße 150, 44801 Bochum, Germany

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

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

Others  :  1087705
DOI  :  10.1186/1471-2105-14-333

 received in 2013-05-03, accepted in 2013-11-07,  发布年份 2013

【 摘 要 】

Background

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.

Results

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.

Conclusions

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.

【 授权许可】

   
2013 Zhong et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

【 参考文献 】

• [1]Lasch P, Haensch W, Lewis EN, Kidder LH, Naumann D: Characterization of colorectal adenocarcinoma sections by spatially resolved ft-ir microspectroscopy. Appl Spectrosc 2002, 56(1):1-9.
• [2]Steller W, Einenkel J, Horn LC, Braumann UD, Binder H, Salzer R, Krafft C: Delimitation of squamous cell cervical carcinoma using infrared microspectroscopic imaging. Anal Bioanal Chem 2006, 384(1):145-154.
• [3]Kallenbach-Thieltges A, Großerüschkamp F, Mosig A, Diem M, Tannapfel A, Gerwert K: Immunohistochemistry, histopathology and infrared spectral histopathology of colon cancer tissue sections. J Biophotonics 2013, 6(1):88-100.
• [4]Trevisan J, Angelov PP, Carmichael PL, Scott AD, Martin FL: Extracting biological information with computational analysis of fourier-transform infrared (ftir) biospectroscopy datasets: current practices to future perspectives. Analyst 2012, 137(14):3202-3215.
• [5]Lasch P, Diem M, Hänsch W, Naumann D: Artificial neural networks as supervised techniques for ft-ir microspectroscopic imaging. J Chemometrics 2006, 20(5):209-220.
• [6]Bird B, Miljkovic M, Romeo MJ, Smith J, Stone N, George MW, Diem M: Infrared micro-spectral imaging: distinction of tissue types in axillary lymph node histology. BMC Clin Pathol 2008, 8(1):8. BioMed Central Full Text
• [7]Lasch P, Haensch W, Naumann D, Diem M: Imaging of colorectal adenocarcinoma using ft-ir microspectroscopy and cluster analysis. Biochimica et Biophysica Acta (BBA)-Molecular Basis of Disease 2004, 1688(2):176-186.
• [8]Kannan S, Ramathilagam S, Sathya A, Pandiyarajan R: Effective fuzzy c-means based kernel function in segmenting medical images. Comput Biol Med 2010, 40(6):572-579.
• [9]Turrell G, Corset J: Raman Microscopy: Developments and Applications. San Diego: Academic Press; 1996.
• [10]Freudiger CW, Min W, Saar BG, Lu S, Holtom GR, He C, Tsai JC, Kang JX, Xie XS: Label-free biomedical imaging with high sensitivity by stimulated raman scattering microscopy. Science 2008, 322(5909):1857-1861.
• [11]Matthäus C, Chernenko T, Newmark JA, Warner CM, Diem M: Label-free detection of mitochondrial distribution in cells by nonresonant raman microspectroscopy. Biophys J 2007, 93(2):668-673.
• [12]Dress A, Lokot T, Schubert W, Serocka P: Two theorems about similarity maps. Ann Combinatorics 2008, 12(3):279-290.
• [13]Serocka P: Visualization of high-dimensional biomedical image data. In Advances in Multimedia Information Processing–PCM 2007. Berlin Heidelberg: Springer; 2007:475-482.
• [14]Schubert W, Gieseler A, Krusche A, Serocka P, Hillert R: Next-generation biomarkers based on 100-parameter functional super-resolution microscopy tis. New Biotechnol 2011, 29(5):599-610.
• [15]Jain AK, Dubes RC: Algorithms for Clustering Data. Prentice-Hall Advanced Reference Series. Upper Saddle River: Prentice Hall PTR; 1988.
• [16]Mosig A, Jäger S, Wang C, Nath S, Ersoy I, Palaniappan K, Chen SS, et al.: Tracking cells in life cell imaging videos using topological alignments. Algorithms Mol Biol 2009, 4(1):10. BioMed Central Full Text
• [17]Xiao H, Li Y, Du J, Mosig A: Ct3d: tracking microglia motility in 3d using a novel cosegmentation approach. Bioinformatics 2011, 27(4):564.
• [18]Halkidi M, Batistakis Y, Vazirgiannis M: On clustering validation techniques. J Intell Inf Syst 2001, 17:107-145.
• [19]Rand WM: Objective criteria for the evaluation of clustering methods. J Am Stat Assoc 1971, 66(336):846-850.
• [20]Hubert L, Arabie P: Comparing partitions. J Classif 1985, 2(1):193-218.
• [21]Meilă M: Comparing clusterings—an information based distance. J Multivariate Anal 2007, 98(5):873-895.
• [22]Friedman J, Hastie T, Tibshirani R: The Elements of Statistical Learning, 2nd edn. New York: Springer; 2008. Chap. 14.3.12 Hierarchical Clustering
• [23]Dotan-Cohen D, Melkman AA, Kasif S: Hierarchical tree snipping: clustering guided by prior knowledge. Bioinformatics 2007, 23(24):3335-3342.
• [24]Navlakha S, White J, Nagarajan N, Pop M, Kingsford C: Finding biologically accurate clusterings in hierarchical tree decompositions using the variation of information. J Comput Biol 2010, 17(3):503-516.
• [25]Bruzzese D, Vistocco D: Cutting the dendrogram through permutation tests. In Proceedings of COMPSTAT’2010. Physica-Verlag HD; 2010:847-854. COMPSTAT 2010 Book of Abstracts, 62
• [26]Ward Jr JH: Hierarchical grouping to optimize an objective function. J Am Stat Assoc 1963, 58(301):236-244.
• [27]Xiao H, Zhang M, Mosig A, Leong H: Dynamic programming algorithms for efficiently computing cosegmentations between biological images. In Algorithms in Bioinformatics. Berlin Heidelberg: Springer; 2011:339-350.
• [28]Wagner S, Wagner D: Comparing clusterings: an overview. Technical Report 2006-4, Universität Karlsruhe, Fakultät für Informatik. 2007
• [29]Peng H, Chung P, Long F, Qu L, Jenett A, Seeds AM, Myers EW, Simpson JH: Brainaligner: 3d registration atlases of drosophila brains. Nat Methods 2011, 8(6):493-498.