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    Automatic identification of overlapping/touching chromosomes in microscopic images using morphological operators

    , Article 2011 7th Iranian Conference on Machine Vision and Image Processing, MVIP 2011 - Proceedings, 16 November 2011 through 17 November 2011 ; November , 2011 , Page(s): 1 - 4 ; 9781457715358 (ISBN) Jahani, S ; Setarehdan, S. K ; Fatemizadeh, E ; Sharif University of Technology
    2011
    Abstract
    Karyotyping, is the process of classification of human chromosomes within the microscopic images. This is a common task for diagnosing many genetic disorders and abnormalities. Automatic Karyotyping algorithms usually suffer the poor quality of the images due to the non rigid nature of the chromosomes which makes them to have unpredictable shapes and sizes in various images. One of the main problems that usually need operator's interaction is the identification and separation of the overlapping/touching chromosomes. This paper presents an effective algorithm for identification of any cluster of the overlapping/touching chromosomes together with the number of chromosomes in the cluster, which... 

    Automated analysis of karyotype images

    , Article Journal of Bioinformatics and Computational Biology ; Volume 20, Issue 3 , 2022 ; 02197200 (ISSN) Khazaei, E ; Emrany, A ; Tavassolipour, M ; Mahjoubi, F ; Ebrahimi, A ; Motahari, S. A ; Sharif University of Technology
    World Scientific  2022
    Abstract
    Karyotype is a genetic test that is used for detection of chromosomal defects. In a karyotype test, an image is captured from chromosomes during the cell division. The captured images are then analyzed by cytogeneticists in order to detect possible chromosomal defects. In this paper, we have proposed an automated pipeline for analysis of karyotype images. There are three main steps for karyotype image analysis: image enhancement, image segmentation and chromosome classification. In this paper, we have proposed a novel chromosome segmentation algorithm to decompose overlapped chromosomes. We have also proposed a CNN-based classifier which outperforms all the existing classifiers. Our...