Literature DB >> 26676686

A review of metaphase chromosome image selection techniques for automatic karyotype generation.

Tanvi Arora1, Renu Dhir2.   

Abstract

The karyotype is analyzed to detect the genetic abnormalities. It is generated by arranging the chromosomes after extracting them from the metaphase chromosome images. The chromosomes are non-rigid bodies that contain the genetic information of an individual. The metaphase chromosome image spread contains the chromosomes, but these chromosomes are not distinct bodies; they can either be individual chromosomes or be touching one another; they may be bent or even may be overlapping and thus forming a cluster of chromosomes. The extraction of chromosomes from these touching and overlapping chromosomes is a very tedious process. The segmentation of a random metaphase chromosome image may not give us correct and accurate results. Therefore, before taking up a metaphase chromosome image for analysis, it must be analyzed for the orientation of the chromosomes it contains. The various reported methods for metaphase chromosome image selection for automatic karyotype generation are compared in this paper. After analysis, it has been concluded that each metaphase chromosome image selection method has its advantages and disadvantages.

Entities:  

Keywords:  Chromosomes; Genetic disorders; Karyotype; Metaphase chromosome images

Mesh:

Year:  2015        PMID: 26676686     DOI: 10.1007/s11517-015-1419-z

Source DB:  PubMed          Journal:  Med Biol Eng Comput        ISSN: 0140-0118            Impact factor:   2.602


  14 in total

1.  Maximum-likelihood techniques for joint segmentation-classification of multispectral chromosome images.

Authors:  Wade C Schwartzkopf; Alan C Bovik; Brian L Evans
Journal:  IEEE Trans Med Imaging       Date:  2005-12       Impact factor: 10.048

2.  Toward a completely automatic neural-network-based human chromosome analysis.

Authors:  B Lerner
Journal:  IEEE Trans Syst Man Cybern B Cybern       Date:  1998

3.  Automated identification of analyzable metaphase chromosomes depicted on microscopic digital images.

Authors:  Xingwei Wang; Shibo Li; Hong Liu; Marc Wood; Wei R Chen; Bin Zheng
Journal:  J Biomed Inform       Date:  2007-07-10       Impact factor: 6.317

4.  Automatic segmentation and disentangling of chromosomes in Q-band prometaphase images.

Authors:  Enrico Grisan; Enea Poletti; Alfredo Ruggeri
Journal:  IEEE Trans Inf Technol Biomed       Date:  2009-02-03

5.  Automatic chromosome pairing using mutual information.

Authors:  Artem Khmelinskii; Rodrigo Ventura; João Sanches
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2008

6.  Automated metaphase finding: an assessment of the efficiency of the METAFER2 system in a routine mutagenicity assay.

Authors:  R Huber; U Kulka; T Lörch; H Braselmann; M Bauchinger
Journal:  Mutat Res       Date:  1995-02       Impact factor: 2.433

7.  Evaluations of auto-focusing methods under a microscopic imaging modality for metaphase chromosome image analysis.

Authors:  Yuchen Qiu; Xiaodong Chen; Yuhua Li; Wei R Chen; Bin Zheng; Shibo Li; Hong Liu
Journal:  Anal Cell Pathol (Amst)       Date:  2013       Impact factor: 2.916

8.  On fully automatic feature measurement for banded chromosome classification.

Authors:  J Piper; E Granum
Journal:  Cytometry       Date:  1989-05

9.  Automated discrimination of dicentric and monocentric chromosomes by machine learning-based image processing.

Authors:  Yanxin Li; Joan H Knoll; Ruth C Wilkins; Farrah N Flegal; Peter K Rogan
Journal:  Microsc Res Tech       Date:  2016-03-01       Impact factor: 2.769

10.  Feature selection for the automated detection of metaphase chromosomes: performance comparison using a receiver operating characteristic method.

Authors:  Yuchen Qiu; Jie Song; Xianglan Lu; Yuhua Li; Bin Zheng; Shibo Li; Hong Liu
Journal:  Anal Cell Pathol (Amst)       Date:  2014-11-11       Impact factor: 2.916

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  2 in total

1.  Correlation-based feature selection and classification via regression of segmented chromosomes using geometric features.

Authors:  Tanvi Arora; Renu Dhir
Journal:  Med Biol Eng Comput       Date:  2016-07-29       Impact factor: 2.602

2.  RC-Net: Regression Correction for End-To-End Chromosome Instance Segmentation.

Authors:  Hui Liu; Guangjie Wang; Sifan Song; Daiyun Huang; Lin Zhang
Journal:  Front Genet       Date:  2022-05-18       Impact factor: 4.772

  2 in total

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