research-article

Artifact removal in radiological ultrasound images using selective and adaptive median filter

Authors:

Anan Nugroho,

Risanuri Hidayat,

Hanung Adi NugrohoAuthors Info & Claims

ICCSP '19: Proceedings of the 3rd International Conference on Cryptography, Security and Privacy

Pages 237 - 241

https://doi.org/10.1145/3309074.3309119

Published: 19 January 2019 Publication History

Get Access

Abstract

Among the existing modalities, ultrasound (US) imaging is mostly utilized to screening abnormalities such as effective used in radiology. Computer-aided diagnosis (CAD) has become a second opinion US reader to improve radiologist's diagnoses by applying appropriate techniques of image processing. The existences of manually induced artifacts, such as caliper marking, rendering and anatomical labeling are negatively affected the results of segmentation in CAD. Moreover improper suppression methods will increase the emergency of undesired artifacts which degrading the quality and clinical applicability of US images. This paper presents the utility of selective and adaptive median filter for manually induced artifacts removal with preserving image features. The experimental results indicate that the proposed method is appropriate to remove artifacts in radiological US images, both in vision and quantitative measurements.

References

[1]

J. Das, B. Das, J. Saikia, and S. Nirmala. Removal of salt and pepper noise using selective adaptive median filter. In 2016 International Conference on Accessibility to Digital World (ICADW), pages 203--206. IEEE, dec 2016.

Crossref

Google Scholar

[2]

A. Elangovan and T. Jeyaseelan. Medical imaging modalities: A survey. In 2016 International Conference on Emerging Trends in Engineering, Technology and Science (ICETETS), pages 1--4. IEEE, feb 2016.

Crossref

Google Scholar

[3]

H. Ibrahim, N. Sia Pik Kong, and T. F. Ng. Simple adaptive median filter for the removal of impulse noise from highly corrupted images. 54:1920 -- 1927, 12 2008.

Digital Library

Google Scholar

[4]

A. Khadem. Artifact suppression in freehand ultrasound elastography using Multiscale Principal Component Analysis. In 2016 23rd Iranian Conference on Biomedical Engineering and 2016 1st International Iranian Conference on Biomedical Engineering (ICBME), number November, pages 176--181. IEEE, 2016.

Crossref

Google Scholar

[5]

N. S. Narayan, P. Marziliano, and C. G. L. Hobbs. Automatic removal of manually induced artefacts in ultrasound images of thyroid gland. In 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), volume 2013, pages 3399--3402. IEEE, jul 2013.

Crossref

Google Scholar

[6]

A. Nugroho, H. A. Nugroho, N. A. Setiawan, and L. Choridah. Internal content classification of ultrasound thyroid nodules based on textural features. Communications in Science and Technology, 1(2):61--69, nov 2016.

Crossref

Google Scholar

[7]

A. Rodriguéz-Cristerna, W. Gómez-Flores, and W. C. de Albuquerque Pereira. A computer-aided diagnosis system for breast ultrasound based on weighted BI-RADS classes. Computer Methods and Programs in Biomedicine, 153:33--40, jan 2018.

Digital Library

Google Scholar

[8]

R. Takahashi and Y. Kajikawa. Computer-aided diagnosis: A survey with bibliometric analysis. International Journal of Medical Informatics, 101:58--67, may 2017.

Crossref

Google Scholar

[9]

P. Tay, S. Acton, and J. Hossack. A Transform Method to Remove Ultrasound Artifacts. In 2006 IEEE Southwest Symposium on Image Analysis and Interpretation, pages 110--114. IEEE, 2006.

Digital Library

Google Scholar

[10]

S.-Q. Yuan and Y.-H. Tan. Impulse noise removal by a globalâĂŞlocal noise detector and adaptive median filter. Signal Processing, 86(8):2123--2128, aug 2006.

Digital Library

Google Scholar

[11]

G. Zhengyang and Z. Le. Improved Adaptive Median Filter. In 2014 Tenth International Conference on Computational Intelligence and Security, pages 44--46. IEEE, nov 2014.

Digital Library

Google Scholar

[12]

Y. Zhu and D. C. Liu. Adaptive Artifact Suppression in Ultrasound Image. In Fourth International Conference on Image and Graphics (ICIG 2007), pages 173--177. IEEE, aug 2007.

Digital Library

Google Scholar

Cited By

View all

Nugroho ASunarko BWibawanto HMulwinda AFauzi AOktaviyanti DSavitri D(2024)Segmentasi Objek Citra Ultrasonografi Terotomatisasi Menggunakan Metode Aktif Kontur KombinatorialJurnal Teknologi dan Sistem Komputer10.14710/jtsiskom.2023.1471211:1Online publication date: 24-Sep-2024
https://doi.org/10.14710/jtsiskom.2023.14712
Gusa RHidayat RNugroho H(2023)Performance Analysis of Enhancement Methods on Fetal Ultrasound Images2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)10.1109/IAICT59002.2023.10205792(326-331)Online publication date: 13-Jul-2023
https://doi.org/10.1109/IAICT59002.2023.10205792
Nugroho AHidayat RAdi Nugroho HDebayle J(2020)Cancerous object detection using morphological region-based active contour in ultrasound imagesJournal of Physics: Conference Series10.1088/1742-6596/1444/1/0120111444:1(012011)Online publication date: 1-Jan-2020
https://doi.org/10.1088/1742-6596/1444/1/012011

Index Terms

Artifact removal in radiological ultrasound images using selective and adaptive median filter
1. Computing methodologies
  1. Computer graphics
    1. Image manipulation
      1. Image processing

Recommendations

Segmentation of the spinous process and its acoustic shadow in vertebral ultrasound images

Spinal ultrasound imaging is emerging as a low-cost, radiation-free alternative to conventional X-ray imaging for the clinical follow-up of patients with scoliosis. Currently, deformity measurement relies almost entirely on manual identification of key ...
Simultaneous lesion segmentation and bias correction in breast ultrasound images
IbPRIA'11: Proceedings of the 5th Iberian conference on Pattern recognition and image analysis

Ultrasound (US) B-mode images often show intensity inhomogeneities caused by an ultrasonic beam attenuation within the body. Due to this artifact, the conventional segmentation approaches based on intensity or intensity-statistics often do not obtain ...
A supervised learning framework for automatic prostate segmentation in trans rectal ultrasound images
ACIVS'12: Proceedings of the 14th international conference on Advanced Concepts for Intelligent Vision Systems

Heterogeneous intensity distribution inside the prostate gland, significant variations in prostate shape, size, inter dataset contrast variations, and imaging artifacts like shadow regions and speckle in Trans Rectal Ultrasound (TRUS) images challenge ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

ICCSP '19: Proceedings of the 3rd International Conference on Cryptography, Security and Privacy

January 2019

303 pages

ISBN:9781450366182

DOI:10.1145/3309074

Conference Chairs:
Yulin Wang
Wuhan University, China
,
Chin-Chen Chang
Feng Chia University, Taiwan

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 January 2019

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

LPDP and the Directorate General of Higher Education, Ministry of Research, Technology and Higher Education, Republic of Indonesia

Conference

ICCSP 2019

ICCSP 2019: 2019 the 3rd International Conference on Cryptography, Security and Privacy

January 19 - 21, 2019

Kuala Lumpur, Malaysia

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
88
Total Downloads

Downloads (Last 12 months)6
Downloads (Last 6 weeks)1

Reflects downloads up to 02 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Nugroho ASunarko BWibawanto HMulwinda AFauzi AOktaviyanti DSavitri D(2024)Segmentasi Objek Citra Ultrasonografi Terotomatisasi Menggunakan Metode Aktif Kontur KombinatorialJurnal Teknologi dan Sistem Komputer10.14710/jtsiskom.2023.1471211:1Online publication date: 24-Sep-2024
https://doi.org/10.14710/jtsiskom.2023.14712
Gusa RHidayat RNugroho H(2023)Performance Analysis of Enhancement Methods on Fetal Ultrasound Images2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)10.1109/IAICT59002.2023.10205792(326-331)Online publication date: 13-Jul-2023
https://doi.org/10.1109/IAICT59002.2023.10205792
Nugroho AHidayat RAdi Nugroho HDebayle J(2020)Cancerous object detection using morphological region-based active contour in ultrasound imagesJournal of Physics: Conference Series10.1088/1742-6596/1444/1/0120111444:1(012011)Online publication date: 1-Jan-2020
https://doi.org/10.1088/1742-6596/1444/1/012011

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Segmentation of the spinous process and its acoustic shadow in vertebral ultrasound images

Simultaneous lesion segmentation and bias correction in breast ultrasound images

A supervised learning framework for automatic prostate segmentation in trans rectal ultrasound images