Abstract
BackgroundMulticolour Fluorescence In-Situ Hybridization (M-FISH) images are employed for detecting chromosomal abnormalities such as chromosomal translocations, deletions, duplication and inversions. This technique uses mixed colours of fluorochromes to paint the whole chromosomes for rapid detection of chromosome rearrangements. The M-FISH data sets used in our research are obtained from microscopic scanning of a metaphase cell labelled with five different fluorochromes and a DAPI staining. The reliability of the technique lies in accurate classification of chromosomes (24 classes for male and 23 classes for female) from M-FISH images. However, due to imaging noise, mis-alignment between multiple channels and many other imaging problems, there is always a classification error, leading to wrong detection of chromosomal abnormalities. Therefore, how to accurately classify different types of chromosomes from M-FISH images becomes a challenging problem.MethodsThis paper presents a novel sparse representation model considering structural information for the classification of M-FISH images. In our previous work a sparse representation based classification model was proposed. This model employed only individual pixel information for the classification. With the structural information of neighbouring pixels as well as the information of themselves simultaneously, the novel approach extended the previous one to the regional case. Based on Orthogonal Matching Pursuit (OMP), we developed simultaneous OMP algorithm (SOMP) to derive an efficient solution of the improved sparse representation model by incorporating the structural information.ResultsThe p-value of two models shows that the newly proposed model incorporating the structural information is significantly superior to our previous one. In addition, we evaluated the effect of several parameters, such as sparsity level, neighbourhood size, and training sample size, on the of the classification accuracy.ConclusionsThe comparison with our previously used sparse model demonstrates that the improved sparse representation model is more effective than the previous one on the classification of the chromosome abnormalities.
Highlights
Multicolour Fluorescence In-Situ Hybridization (M-FISH) images are employed for detecting chromosomal abnormalities such as chromosomal translocations, deletions, duplication and inversions
Multiple Measurement Vectors (MMV) based models have been proposed to recover a set of vectors that share a common support. Such models can find wide applications in many research fields (e.g., multiple signal classification(MUSIC)[18], blind multiband signal reconstruction[19] and compressive diffuse optical tomography[20]), where MMV problem is commonly applied. Motivated by these efforts on the MMV problem, we proposed a novel sparse representation model by incorporating the structural information into the classification of M-FISH image set, which was reported in our preliminary study [21]
M-FISH database We have collaborated with Advanced Digital Imaging Research (ADIR; League City, Texas, USA) to establish the M-FISH image database, which is a valuable source for chromosome imaging studies [18]
Summary
Multicolour Fluorescence In-Situ Hybridization (M-FISH) images are employed for detecting chromosomal abnormalities such as chromosomal translocations, deletions, duplication and inversions This technique uses mixed colours of fluorochromes to paint the whole chromosomes for rapid detection of chromosome rearrangements. Chromosomal abnormalities (e.g., changes in number and translocations of structures) could all cause genetic diseases and cancers To detect these deathful diseases, multicolour Fluorescence In-Situ Hybridization (MFISH) technique use different colours to paint human chromosomes. This technique can be employed to analyze these abnormalities simultaneously [1,2]. This cytogenetic approach uses N fluorochromes to label a metaphase cell; there are 2N-1 different combinations that can differentiate different types of chromosomes. It is extremely challenging to identify the chromosomes accurately based on M-FISH image set in practice
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
