Research Article

M-FISH Image Segmentation and Classification using Fuzzy Logic

by  Lijiya A, Sreejithlal G S, Govindan V K
journal cover
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 70 - Issue 25
Published: May 2013
Authors: Lijiya A, Sreejithlal G S, Govindan V K
10.5120/12227-8519
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Lijiya A, Sreejithlal G S, Govindan V K . M-FISH Image Segmentation and Classification using Fuzzy Logic. International Journal of Computer Applications. 70, 25 (May 2013), 46-51. DOI=10.5120/12227-8519

                        @article{ 10.5120/12227-8519,
                        author  = { Lijiya A,Sreejithlal G S,Govindan V K },
                        title   = { M-FISH Image Segmentation and Classification using Fuzzy Logic },
                        journal = { International Journal of Computer Applications },
                        year    = { 2013 },
                        volume  = { 70 },
                        number  = { 25 },
                        pages   = { 46-51 },
                        doi     = { 10.5120/12227-8519 },
                        publisher = { Foundation of Computer Science (FCS), NY, USA }
                        }
                        %0 Journal Article
                        %D 2013
                        %A Lijiya A
                        %A Sreejithlal G S
                        %A Govindan V K
                        %T M-FISH Image Segmentation and Classification using Fuzzy Logic%T 
                        %J International Journal of Computer Applications
                        %V 70
                        %N 25
                        %P 46-51
                        %R 10.5120/12227-8519
                        %I Foundation of Computer Science (FCS), NY, USA
Abstract

Karyotyping has an important role in identifying genetic disorders due to structural changes in chromosomes. Multiplex fluorescence in-situ hybridization (M-FISH) technique provides more precise karyotyping. The new classification method, proposed in this paper, automates karyotyping, based on Fuzzy c-means (FCM) algorithm combined with a labeling chart. Classification results show that the proposed method improves accuracy and running time. It is also observed that the accuracy of classification can further be improved, using a new Reclassification algorithm which reduces the chance of wrongly classified chromosome pixels.

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Index Terms
Computer Science
Information Sciences
No index terms available.
Keywords

Karyotyping Multiplex fluorescence in-situ hybridization (M-FISH) Fuzzy c-means (FCM) Labeling chart Reclassification

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