The use of routine karyotyping can easily pick up r(20); this information is especially useful in resource-poor countries. We have evolved an algorithm stating the indications to attempt r(20) karyotyping in a given patient in the light of the results of the present study and the existing literature.

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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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Genetic disorders and malignancies due to the chromosomal abnormalities are being researched in cytogenetics till date. G-banded metaphase images are analyzed, chromosomes pairs are identified and arranged into 23 classes as per ISCN ideogram features through karyotyping. This enables the cytogenetic experts to visualize and detect the chromosomal aberrations at ease. Although, the design of a fully automated karyotyping system is difficult, it eliminates the barriers of manual karyotyping. Here, we propose preprocessing techniques for G-banded metaphase images for the design of automated karyotyping system. Our method starts with a decision tree classifier that classifies the input images into analyzable and un-analyzable. Analyzable metaphase images are denoised by median filter and bilateral filter. Denoised images are enhanced using Iterative contrast limited adaptive histogram equalization and are segmented based on contour. Our method ends with an ANN classifier that classifies the segmented images into single straight, bended, touching and overlapped based on the top ten Chi square selected GLCM geometrical features.

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The results are consistent with the number of patches used, and they demonstrate that it is possible to use motion estimation techniques and "recycle" photons from previous frames to improve the image quality of the current frame. Our results are comparable in terms of CNR to Video Block Matching 3D-a state-of-the-art denoising method. But qualitative analysis by experts confirms that the denoised ultra-low dose X-ray images obtained using our method are more realistic with respect to appearance.

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