Survey on Chromosome Image Analysis for Abnormality Detection in Leukemias

Vijayan, Vinya; Remya, R.; Sabeena, K

doi:10.15623/ijret.2015.0404114

Cited by 8 publications

References 23 publications

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Deep learning‐based semantic segmentation of interphase cells and debris from metaphase images

Sathyan

Menon

Prasad³

et al. 2022

Int J Imaging Syst Tech

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Segmentation plays an essential role in the design of the automated karyotyping system (AKS). It is pivotal to segment interphase cells and other debris usually found in the input G metaphase images. The performance of AKSs is considerably less when interphase cells and debris are present in the input images. In this article, two semantic segmentation models are proposed. For this experiment, an annotated dataset is generated from the G banded metaphase images which are prepared at Regional Cancer Centre (RCC), Thiruvananthapuram, Kerala, India. Inspired by the results of UNet, a lighter version L‐UNet is developed and experimented with. It shows the validation IoU (Intersection over Union) of 0.9809 and F1‐score of 0.9903 on the RCC dataset and the test IoU of 0.9720 and F1‐score of 0.9858 on the CRCN‐NE dataset. As backbone semantic segmentation models are state of the art, an efficient model, Eff‐UNet, is also proposed here. In this model, EfficientNetB03 acts as the backbone that extracts powerful features and UNet acts as the decoder that predicts the segmentation map. It performs with the validation IoU of 0.9842 and F1‐score of 0.9920 on the RCC dataset and the test IoU of 0.7545 and F1‐score of 0.7778 on the CRCN‐NE dataset. To derive this model, 25 encoder–decoder architectures are evaluated with various top‐performing CNNs (convolutional neural networks) as encoders and segmentation networks as decoders. Results are further compared with various segmentation models and the best results are obtained from the proposed model.

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Deep learning‐based semantic segmentation of interphase cells and debris from metaphase images

Sathyan

Menon

Prasad³

et al. 2022

Int J Imaging Syst Tech

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

Arora

Dhir

2015

Med Biol Eng Comput

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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.

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New algorithm for separation overlapping & touching chromosomes

Al-Ameri

Al-Hameed

2020

J. Phys.: Conf. Ser.

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The karyotyping technique is importance in the diagnosis of genetic diseases of humans, including the diagnosis the genetic disorders of prenatal and cancer. In order to obtain the karyotype system for the metaphase images of chromosomes using the image processing. Assembling of human chromosomes pairs from the metaphase image will pass in several stages which are segmentation (single chromosome, overlapped and touching chromosome), feature extraction, matching and classification. In this study has been focus on the separation of touching and overlapped. The problem of separation touching and overlapped chromosome was solved using the convulsion mask and with a help the morphological thinning and contour. The separation of touching and overlapped chromosome is according on finding cut points. The thinning of the image is obtained which helps to identification the Region of Interest. The contour of the image is obtained which helps to explain the entire shape of the image and find out the cut points for separation between chromosomes. In this study an efficient algorithms was used to separation the cluster of touching and overlapping chromosomes. The algorithm of separation touching and overlapping was implemented by using sequential stages (initially, segmentation from metaphase image, convert to the binary image, extract thinning, using mask with 9*9, extract contour, using mask 7*7, identified the cut points and finally, separate touching or overlapping chromosome). This algorithm capable to isolate a cluster of touching chromosomes and a cluster of touching and overlapping chromosomes.

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Survey on Chromosome Image Analysis for Abnormality Detection in Leukemias

Cited by 8 publications

References 23 publications

Deep learning‐based semantic segmentation of interphase cells and debris from metaphase images

Deep learning‐based semantic segmentation of interphase cells and debris from metaphase images

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

New algorithm for separation overlapping & touching chromosomes

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