Computer Aided Method for the Detection of Structural Abnormalities in Acute Lymphocytic Leukemia from the G-Banded Karyotypes

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

doi:10.1109/cetic4.2018.8531008

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…It is only the classification which leads to the preparation of karyogram and enables cytogenetic experts to analyse it for chromosome abnormality detection. Some researchers have tried to automate chromosome abnormality detection (Vanitha & Venmathi, 2011; Remya et al, 2016, 2018). Chromosome classification is one of the research areas, which is exhaustively investigated.…”

Section: Traditional Techniques For Aks Designmentioning

confidence: 99%

Traditional and deep‐based techniques for end‐to‐end automated karyotyping: A review

et al. 2021

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In the field of cytogenetics, chromosome image analysis or karyotyping from metaphase images plays an imperative role in the diagnosis, prognosis and treatment assessment of different genetic disorders and cancers. This paper is a comprehensive review on different traditional and deep‐based techniques, which are utilized in the design of automated karyotyping systems (AKSs). By this review, a detailed methodology is suggested for the design of end‐to‐end automated karyotyping system (EEAKS) which portrays a sequential multi stage approach. Methods related to all the stages in EEAKS are systematically surveyed by exploring the state of the art literature. Datasets and performance measures incorporated in the past studies are explored. Even though numerous methods were proposed throughout the past three decades, a completely automated framework has not yet been acknowledged. Inferences from this study show that, while various traditional image processing strategies are utilized for pre‐processing and segmentation, machine learning techniques are used only for the classification purpose. In conventional classifiers, artificial neural networks are generally utilized even when the peak performance is given by support vector machines. However, owing to the recent prodigious breakthrough in computer vision, deep neural networks are progressively utilized for developing automated systems. It is seen that deep neural networks are not yet explored in the realm of pre‐processing stage of EEAKS. However, limited number of methods based on convolutional neural networks (CNN) are utilized in all other stages. This review recommends a hybrid CNN for the design of EEAKS, in which all the stages can be automated by sub CNNs. Methodology for generating sufficient datasets is also discussed here which is, indeed, required for further research in this area. This paper concludes with future research directions for the development of a fully automated end‐to‐end karyotyping system.

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Section: Traditional Techniques For Aks Designmentioning

confidence: 99%

Traditional and deep‐based techniques for end‐to‐end automated karyotyping: A review

et al. 2021

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Detecting Philadelphia Chromosome on Metaphase Images using a Convolutional Neural Network

Pravalphruekul

Tangpornpisit

Limtoprasert

et al. 2020

2020 1st International Conference on Big Data Analytics and Practices (IBDAP)

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An efficient algorithm for automatic classification and centromere detection in G-band human chromosome image using band distance feature

Kumar

Vasuki²

2021

IFS

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In chromosome analysis, centromere is an essential component. By analyzing centromere, genetic disorder can be identified easily. In this paper, automatic classification and centromere detection in human chromosome image using Band Distance Feature is proposed. Initially the microscopic image of G-band chromosome is preprocessed in order to remove the blobs. Then, the image is segmented using labelling algorithm and the endpoints are calculated. Now, the overlapping chromosomes are removed when the number of end points is greater than two. The non-overlapped chromosomes are straightened using Reversible Projection algorithm. From the straightened chromosome band distance feature is calculated. The extracted features are given to the ANN classifier to identify the class of chromosome and to calculate the centromere. From the experimental results, it is observed that the proposed method is superior to the traditional method.

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Computer Aided Method for the Detection of Structural Abnormalities in Acute Lymphocytic Leukemia from the G-Banded Karyotypes

Cited by 3 publications

References 7 publications

Traditional and deep‐based techniques for end‐to‐end automated karyotyping: A review

Traditional and deep‐based techniques for end‐to‐end automated karyotyping: A review

Detecting Philadelphia Chromosome on Metaphase Images using a Convolutional Neural Network

An efficient algorithm for automatic classification and centromere detection in G-band human chromosome image using band distance feature

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