A Hybrid Approach of Using Wavelets and Fuzzy Clustering for Classifying Multispectral Florescence In Situ Hybridization Images
Multicolor or multiplex fluorescence in situ hybridization (M-FISH) imaging is a recently developed molecular cytogenetic diagnosis technique for rapid visualization of genomic aberrations at the chromosomal level. By the simultaneous use of all 24 human chromosome painting probes, M-FISH imaging fa...
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| Format: | Article |
| Language: | English |
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Wiley
2006-01-01
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| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://dx.doi.org/10.1155/IJBI/2006/54532 |
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| author | Yu-Ping Wang Ashok Kumar Dandpat |
| author_facet | Yu-Ping Wang Ashok Kumar Dandpat |
| author_sort | Yu-Ping Wang |
| collection | DOAJ |
| description | Multicolor or multiplex fluorescence in situ
hybridization (M-FISH) imaging is a recently developed molecular
cytogenetic diagnosis technique for rapid visualization of genomic
aberrations at the chromosomal level. By the simultaneous use of
all 24 human chromosome painting probes, M-FISH imaging
facilitates precise identification of complex chromosomal
rearrangements that are responsible for cancers and genetic
diseases. The current approaches, however, cannot have the
precision sufficient for clinical use. The reliability of the
technique depends primarily on the accurate pixel-wise
classification, that is, assigning each pixel into one of the 24
classes of chromosomes based on its six-channel spectral
representations. In the paper we introduce a novel approach to
improve the accuracy of pixel-wise classification. The approach is
based on the combination of fuzzy clustering and wavelet
normalization. Two wavelet-based algorithms are used to reduce
redundancies and to correct misalignments between multichannel
FISH images. In comparison with conventional algorithms, the
wavelet-based approaches offer more advantages such as the
adaptive feature selection and accurate image registration. The
algorithms have been tested on images from normal cells, showing
the improvement in classification accuracy. The increased accuracy
of pixel-wise classification will improve the reliability of the
M-FISH imaging technique in identifying subtle and cryptic
chromosomal abnormalities for cancer diagnosis and genetic
disorder research. |
| format | Article |
| id | doaj-art-510b106c677d4358a7431e1a9c59330d |
| institution | Kabale University |
| issn | 1687-4188 1687-4196 |
| language | English |
| publishDate | 2006-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Biomedical Imaging |
| spelling | doaj-art-510b106c677d4358a7431e1a9c59330d2025-02-03T01:21:38ZengWileyInternational Journal of Biomedical Imaging1687-41881687-41962006-01-01200610.1155/IJBI/2006/5453254532A Hybrid Approach of Using Wavelets and Fuzzy Clustering for Classifying Multispectral Florescence In Situ Hybridization ImagesYu-Ping Wang0Ashok Kumar Dandpat1Computer Science and Electrical Engineering Department, School of Computing and Engineering, University of Missouri-Kansas City, MO 64110, USAComputer Science and Electrical Engineering Department, School of Computing and Engineering, University of Missouri-Kansas City, MO 64110, USAMulticolor or multiplex fluorescence in situ hybridization (M-FISH) imaging is a recently developed molecular cytogenetic diagnosis technique for rapid visualization of genomic aberrations at the chromosomal level. By the simultaneous use of all 24 human chromosome painting probes, M-FISH imaging facilitates precise identification of complex chromosomal rearrangements that are responsible for cancers and genetic diseases. The current approaches, however, cannot have the precision sufficient for clinical use. The reliability of the technique depends primarily on the accurate pixel-wise classification, that is, assigning each pixel into one of the 24 classes of chromosomes based on its six-channel spectral representations. In the paper we introduce a novel approach to improve the accuracy of pixel-wise classification. The approach is based on the combination of fuzzy clustering and wavelet normalization. Two wavelet-based algorithms are used to reduce redundancies and to correct misalignments between multichannel FISH images. In comparison with conventional algorithms, the wavelet-based approaches offer more advantages such as the adaptive feature selection and accurate image registration. The algorithms have been tested on images from normal cells, showing the improvement in classification accuracy. The increased accuracy of pixel-wise classification will improve the reliability of the M-FISH imaging technique in identifying subtle and cryptic chromosomal abnormalities for cancer diagnosis and genetic disorder research.http://dx.doi.org/10.1155/IJBI/2006/54532 |
| spellingShingle | Yu-Ping Wang Ashok Kumar Dandpat A Hybrid Approach of Using Wavelets and Fuzzy Clustering for Classifying Multispectral Florescence In Situ Hybridization Images International Journal of Biomedical Imaging |
| title | A Hybrid Approach of Using Wavelets and Fuzzy Clustering for Classifying Multispectral Florescence In Situ Hybridization Images |
| title_full | A Hybrid Approach of Using Wavelets and Fuzzy Clustering for Classifying Multispectral Florescence In Situ Hybridization Images |
| title_fullStr | A Hybrid Approach of Using Wavelets and Fuzzy Clustering for Classifying Multispectral Florescence In Situ Hybridization Images |
| title_full_unstemmed | A Hybrid Approach of Using Wavelets and Fuzzy Clustering for Classifying Multispectral Florescence In Situ Hybridization Images |
| title_short | A Hybrid Approach of Using Wavelets and Fuzzy Clustering for Classifying Multispectral Florescence In Situ Hybridization Images |
| title_sort | hybrid approach of using wavelets and fuzzy clustering for classifying multispectral florescence in situ hybridization images |
| url | http://dx.doi.org/10.1155/IJBI/2006/54532 |
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