Literature DB >> 7851156

Fully automatic chromosome segmentation.

L Ji1.   

Abstract

This paper is concerned with automatic segmentation of high resolution digitized metaphases. This includes automatic detection and rejection of interphase nuclei, stain debris, and other "noise"; automatic detection and segmentation of touching and overlapping chromosome clusters; and automatic rejection of cells which are evaluated as being incomplete, or incorrectly segmented, or where the cell is otherwise unsuitable for further analysis. In this paper, a rule-based approach is described which treats the cell as a whole rather than as a series of individual chromosomes or clusters. The rules adapt classification and segmentation parameters for each cell. Initially, different sets of parameters are chosen according to the staining method of the cells, and the goal of the segmentation. A chromosome number predictor is used to guide the adaptation of the parameters and to estimate the performance. The adaptation is iterative, and the self-adjustment will stop when either a satisfactory result is achieved or if the cell is rejected. The method was implemented on both a Sun workstation and a Cytoscan, a commercial machine for chromosome analysis. Seven hundred and thirteen cells from real data have been tested. A success rate of 90-95% has been achieved. The procedure has been implemented in an automatic aberration scoring system for routine use.

Mesh:

Year:  1994        PMID: 7851156     DOI: 10.1002/cyto.990170303

Source DB:  PubMed          Journal:  Cytometry        ISSN: 0196-4763


  3 in total

1.  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.  Development and Assessment of an Integrated Computer-Aided Detection Scheme for Digital Microscopic Images of Metaphase Chromosomes.

Authors:  Xingwei Wang; Bin Zheng; Shibo Li; John J Mulvihill; Hong Liu
Journal:  J Electron Imaging       Date:  2008-11-12       Impact factor: 0.945

3.  A dicentric chromosome identification method based on clustering and watershed algorithm.

Authors:  Xiang Shen; Yafeng Qi; Tengfei Ma; Zhenggan Zhou
Journal:  Sci Rep       Date:  2019-02-19       Impact factor: 4.379

  3 in total

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