Chromosome Segmentation Analysis Using Image Processing Techniques and Autoencoders

Amritha Pallavoor; Prajwal Anagani; Sundareshan Tambarahalli; Sreekanth Pallavoor

doi:doi:10.11648/j.ajcst.20220504.13

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Chromosome Segmentation Analysis Using Image Processing Techniques and Autoencoders

Amritha Pallavoor, Prajwal Anagani, Sundareshan Tambarahalli, Sreekanth Pallavoor

Published in American Journal of Computer Science and Technology (Volume 5, Issue 4)

Received: 14 October 2022 Accepted: 10 November 2022 Published: 23 November 2022

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Abstract

Chromosome analysis and identification from metaphase images is a critical part of cytogenetics based medical diagnosis. It is mainly used for identifying constitutional, prenatal and acquired abnormalities in the diagnosis of genetic diseases and disorders. The process of identification of chromosomes from metaphase is a tedious one and requires trained personnel and several hours to perform. Challenge exists especially in handling touching, overlapping and clustered chromosomes in metaphase images, which if not segmented properly would result in wrong classification. This study proposes a method to automate the process of detection and segmentation of chromosomes from a given metaphase image, and in using them to classify through a Deep CNN architecture to know the chromosome type. There are two methods to handle the separation of overlapping chromosomes found in metaphases - one method involving watershed algorithm followed by autoencoders and the other a method purely based on watershed algorithm. These methods involve a combination of automation and very minimal manual effort to perform the segmentation, which produces the output. The manual effort ensures that human intuition is taken into consideration, especially in handling touching, overlapping and cluster chromosomes. Upon segmentation, individual chromo- some images are then classified into their respective classes with 95.75% accuracy using a Deep CNN model. Further, a distribution strategy is imparted to classify these chromosomes from the given output (which typically could consist of 46 individual images in a normal scenario for human beings) into its individual classes with an accuracy of 98%. This study helps conclude that pure manual effort involved in chromosome segmentation can be automated to a very good level through image processing techniques to produce reliable and satisfying results.

Published in	American Journal of Computer Science and Technology (Volume 5, Issue 4)
DOI	10.11648/j.ajcst.20220504.13
Page(s)	210-216
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2022. Published by Science Publishing Group

Keywords

Chromosome Analysis, Karyotyping, Cytogenetics, Chromosome Segmentation, Autoencoder, Squeezenet, Watershed Algorithm

References

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[2]	M. S. Al-Kharraz, L. A. Elrefaei and M. A. Fadel, ”Automated System for Chromosome Karyotyping to Recognize the Most Common Numerical Abnormalities Using Deep Learning,” in IEEE Access, vol. 8, pp. 157727-157747, 2020, doi: 10.1109/ACCESS.2020.3019937.
[3]	H. M. Saleh, N. H. Saad, N. A. Mat Isa, ”Overlapping Chromosome Segmentation Using UNET: Convolutional Networks with Test Time Augmentation ” in Procedia Computer Science, Vol 159, pp. 524-533, 2019.
[4]	H. A. Al-Ameri and W. Al-Hameed, ”New algorithm for separation overlapping touching chromosomes”, 2020 J. Phys.: Conf. Ser. 1530.
[5]	E. Poletti, E. Grisan, A. Ruggeri, ”Automatic classification of chromo- somes in Q-band images” in Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2008, 1911–1914.
[6]	E. Poletti, E. Grisan, A. Ruggeri, ”A modular framework for the automatic classification of chromosomes in Q-band images”, Computer Methods and Programs in Biomedicine, Vol 105, pp. 120-130, 2012.
[7]	M. Al-Kharraz, L. A. Elrefaei, M. Fadel, “Classifying Chromosome Images Using Ensemble Convolutional Neural Networks” in Applications of Artificial Intelligence in Engineering, pp. 751, 2021.
[8]	S. Swati, M. Sharma and L. Vig, ”Automatic Classification of Low- Resolution Chromosomal Images”, in Computer Vision – ECCV 2018 Workshops, Vol 11134, pp. 315-325, 2019.
[9]	X. Liu, L. Fu, J. Chun-Wei Lin, S. Liu ”SRAS-net: low-resolution chromosome image classification based on deep learning” in IET Systems Biology, 16 (3-4), 85–97 (2022). https://doi.org/10.1049/syb2.12042
[10]	E. Grisan, E. Poletti, A. Ruggeri, ”Automatic segmentation and disen- tangling of chromosome in Q-band prometaphase images”, IEEE Trans Inf Technol B, 2009.
[11]	F. N. Iandola, S. Han, M. W. Moskewicz, K. Ashraf, W. J. Dally, K. Keutzer, ”Squeezenet: AlexNet-level accuracy with 50x fewer parameters and ¡0.5MB model size”, arXiv: 1602.07360, Nov 2016.
[12]	A. Krizhevsky, I. Sutskever, G. E. Hinton, ”ImageNet Classification with Deep Convolutional Neural Networks” in Communications of the ACM, Vol 60, Issue 6, June 2017 pp 84–90, https://doi.org/10.1145/3065386
[13]	J. Canny, ”A Computational Approach to Edge Detection,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI- 8, no. 6, pp. 679-698, Nov. 1986, doi: 10.1109/TPAMI.1986.4767851.
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APA Style

Amritha Pallavoor, Prajwal Anagani, Sundareshan Tambarahalli, Sreekanth Pallavoor. (2022). Chromosome Segmentation Analysis Using Image Processing Techniques and Autoencoders. American Journal of Computer Science and Technology, 5(4), 210-216. https://doi.org/10.11648/j.ajcst.20220504.13

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ACS Style

Amritha Pallavoor; Prajwal Anagani; Sundareshan Tambarahalli; Sreekanth Pallavoor. Chromosome Segmentation Analysis Using Image Processing Techniques and Autoencoders. Am. J. Comput. Sci. Technol. 2022, 5(4), 210-216. doi: 10.11648/j.ajcst.20220504.13

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AMA Style

Amritha Pallavoor, Prajwal Anagani, Sundareshan Tambarahalli, Sreekanth Pallavoor. Chromosome Segmentation Analysis Using Image Processing Techniques and Autoencoders. Am J Comput Sci Technol. 2022;5(4):210-216. doi: 10.11648/j.ajcst.20220504.13

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@article{10.11648/j.ajcst.20220504.13,
  author = {Amritha Pallavoor and Prajwal Anagani and Sundareshan Tambarahalli and Sreekanth Pallavoor},
  title = {Chromosome Segmentation Analysis Using Image Processing Techniques and Autoencoders},
  journal = {American Journal of Computer Science and Technology},
  volume = {5},
  number = {4},
  pages = {210-216},
  doi = {10.11648/j.ajcst.20220504.13},
  url = {https://doi.org/10.11648/j.ajcst.20220504.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajcst.20220504.13},
  abstract = {Chromosome analysis and identification from metaphase images is a critical part of cytogenetics based medical diagnosis. It is mainly used for identifying constitutional, prenatal and acquired abnormalities in the diagnosis of genetic diseases and disorders. The process of identification of chromosomes from metaphase is a tedious one and requires trained personnel and several hours to perform. Challenge exists especially in handling touching, overlapping and clustered chromosomes in metaphase images, which if not segmented properly would result in wrong classification. This study proposes a method to automate the process of detection and segmentation of chromosomes from a given metaphase image, and in using them to classify through a Deep CNN architecture to know the chromosome type. There are two methods to handle the separation of overlapping chromosomes found in metaphases - one method involving watershed algorithm followed by autoencoders and the other a method purely based on watershed algorithm. These methods involve a combination of automation and very minimal manual effort to perform the segmentation, which produces the output. The manual effort ensures that human intuition is taken into consideration, especially in handling touching, overlapping and cluster chromosomes. Upon segmentation, individual chromo- some images are then classified into their respective classes with 95.75% accuracy using a Deep CNN model. Further, a distribution strategy is imparted to classify these chromosomes from the given output (which typically could consist of 46 individual images in a normal scenario for human beings) into its individual classes with an accuracy of 98%. This study helps conclude that pure manual effort involved in chromosome segmentation can be automated to a very good level through image processing techniques to produce reliable and satisfying results.},
 year = {2022}
}

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TY  - JOUR
T1  - Chromosome Segmentation Analysis Using Image Processing Techniques and Autoencoders
AU  - Amritha Pallavoor
AU  - Prajwal Anagani
AU  - Sundareshan Tambarahalli
AU  - Sreekanth Pallavoor
Y1  - 2022/11/23
PY  - 2022
N1  - https://doi.org/10.11648/j.ajcst.20220504.13
DO  - 10.11648/j.ajcst.20220504.13
T2  - American Journal of Computer Science and Technology
JF  - American Journal of Computer Science and Technology
JO  - American Journal of Computer Science and Technology
SP  - 210
EP  - 216
PB  - Science Publishing Group
SN  - 2640-012X
UR  - https://doi.org/10.11648/j.ajcst.20220504.13
AB  - Chromosome analysis and identification from metaphase images is a critical part of cytogenetics based medical diagnosis. It is mainly used for identifying constitutional, prenatal and acquired abnormalities in the diagnosis of genetic diseases and disorders. The process of identification of chromosomes from metaphase is a tedious one and requires trained personnel and several hours to perform. Challenge exists especially in handling touching, overlapping and clustered chromosomes in metaphase images, which if not segmented properly would result in wrong classification. This study proposes a method to automate the process of detection and segmentation of chromosomes from a given metaphase image, and in using them to classify through a Deep CNN architecture to know the chromosome type. There are two methods to handle the separation of overlapping chromosomes found in metaphases - one method involving watershed algorithm followed by autoencoders and the other a method purely based on watershed algorithm. These methods involve a combination of automation and very minimal manual effort to perform the segmentation, which produces the output. The manual effort ensures that human intuition is taken into consideration, especially in handling touching, overlapping and cluster chromosomes. Upon segmentation, individual chromo- some images are then classified into their respective classes with 95.75% accuracy using a Deep CNN model. Further, a distribution strategy is imparted to classify these chromosomes from the given output (which typically could consist of 46 individual images in a normal scenario for human beings) into its individual classes with an accuracy of 98%. This study helps conclude that pure manual effort involved in chromosome segmentation can be automated to a very good level through image processing techniques to produce reliable and satisfying results.
VL  - 5
IS  - 4
ER  -

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Author Information

Amritha Pallavoor

Department of Computer Science & Engineering, PES University, Bangalore, India
Prajwal Anagani

Department of Computer Science & Engineering, PES University, Bangalore, India
Sundareshan Tambarahalli

Dr. Rao’s Genetics Laboratory and Research Center, Bangalore, India
Sreekanth Pallavoor

CARAIO Technologies, Bangalore, India

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APA Style

Amritha Pallavoor, Prajwal Anagani, Sundareshan Tambarahalli, Sreekanth Pallavoor. (2022). Chromosome Segmentation Analysis Using Image Processing Techniques and Autoencoders. American Journal of Computer Science and Technology, 5(4), 210-216. https://doi.org/10.11648/j.ajcst.20220504.13

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ACS Style

Amritha Pallavoor; Prajwal Anagani; Sundareshan Tambarahalli; Sreekanth Pallavoor. Chromosome Segmentation Analysis Using Image Processing Techniques and Autoencoders. Am. J. Comput. Sci. Technol. 2022, 5(4), 210-216. doi: 10.11648/j.ajcst.20220504.13

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AMA Style

Amritha Pallavoor, Prajwal Anagani, Sundareshan Tambarahalli, Sreekanth Pallavoor. Chromosome Segmentation Analysis Using Image Processing Techniques and Autoencoders. Am J Comput Sci Technol. 2022;5(4):210-216. doi: 10.11648/j.ajcst.20220504.13

Copy | Download

@article{10.11648/j.ajcst.20220504.13,
  author = {Amritha Pallavoor and Prajwal Anagani and Sundareshan Tambarahalli and Sreekanth Pallavoor},
  title = {Chromosome Segmentation Analysis Using Image Processing Techniques and Autoencoders},
  journal = {American Journal of Computer Science and Technology},
  volume = {5},
  number = {4},
  pages = {210-216},
  doi = {10.11648/j.ajcst.20220504.13},
  url = {https://doi.org/10.11648/j.ajcst.20220504.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajcst.20220504.13},
  abstract = {Chromosome analysis and identification from metaphase images is a critical part of cytogenetics based medical diagnosis. It is mainly used for identifying constitutional, prenatal and acquired abnormalities in the diagnosis of genetic diseases and disorders. The process of identification of chromosomes from metaphase is a tedious one and requires trained personnel and several hours to perform. Challenge exists especially in handling touching, overlapping and clustered chromosomes in metaphase images, which if not segmented properly would result in wrong classification. This study proposes a method to automate the process of detection and segmentation of chromosomes from a given metaphase image, and in using them to classify through a Deep CNN architecture to know the chromosome type. There are two methods to handle the separation of overlapping chromosomes found in metaphases - one method involving watershed algorithm followed by autoencoders and the other a method purely based on watershed algorithm. These methods involve a combination of automation and very minimal manual effort to perform the segmentation, which produces the output. The manual effort ensures that human intuition is taken into consideration, especially in handling touching, overlapping and cluster chromosomes. Upon segmentation, individual chromo- some images are then classified into their respective classes with 95.75% accuracy using a Deep CNN model. Further, a distribution strategy is imparted to classify these chromosomes from the given output (which typically could consist of 46 individual images in a normal scenario for human beings) into its individual classes with an accuracy of 98%. This study helps conclude that pure manual effort involved in chromosome segmentation can be automated to a very good level through image processing techniques to produce reliable and satisfying results.},
 year = {2022}
}

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TY  - JOUR
T1  - Chromosome Segmentation Analysis Using Image Processing Techniques and Autoencoders
AU  - Amritha Pallavoor
AU  - Prajwal Anagani
AU  - Sundareshan Tambarahalli
AU  - Sreekanth Pallavoor
Y1  - 2022/11/23
PY  - 2022
N1  - https://doi.org/10.11648/j.ajcst.20220504.13
DO  - 10.11648/j.ajcst.20220504.13
T2  - American Journal of Computer Science and Technology
JF  - American Journal of Computer Science and Technology
JO  - American Journal of Computer Science and Technology
SP  - 210
EP  - 216
PB  - Science Publishing Group
SN  - 2640-012X
UR  - https://doi.org/10.11648/j.ajcst.20220504.13
AB  - Chromosome analysis and identification from metaphase images is a critical part of cytogenetics based medical diagnosis. It is mainly used for identifying constitutional, prenatal and acquired abnormalities in the diagnosis of genetic diseases and disorders. The process of identification of chromosomes from metaphase is a tedious one and requires trained personnel and several hours to perform. Challenge exists especially in handling touching, overlapping and clustered chromosomes in metaphase images, which if not segmented properly would result in wrong classification. This study proposes a method to automate the process of detection and segmentation of chromosomes from a given metaphase image, and in using them to classify through a Deep CNN architecture to know the chromosome type. There are two methods to handle the separation of overlapping chromosomes found in metaphases - one method involving watershed algorithm followed by autoencoders and the other a method purely based on watershed algorithm. These methods involve a combination of automation and very minimal manual effort to perform the segmentation, which produces the output. The manual effort ensures that human intuition is taken into consideration, especially in handling touching, overlapping and cluster chromosomes. Upon segmentation, individual chromo- some images are then classified into their respective classes with 95.75% accuracy using a Deep CNN model. Further, a distribution strategy is imparted to classify these chromosomes from the given output (which typically could consist of 46 individual images in a normal scenario for human beings) into its individual classes with an accuracy of 98%. This study helps conclude that pure manual effort involved in chromosome segmentation can be automated to a very good level through image processing techniques to produce reliable and satisfying results.
VL  - 5
IS  - 4
ER  -

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