Abnormality detection for infection and fluid cases in chest radiograph

Wan Siti Halimatul Munirah Wan Ahmad, Mohammad Faizal Ahmad Fauzi, Wan Mimi Diyana Wan Zaki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

This paper presents an automated abnormality detection system for infection and fluid cases in the lung field for chest radiograph. The abnormality features represented as abnormality scores are investigated based on the sharpness of costophrenic angle (Scoreθn), symmetrical lung area (ScoreLp), area of the lung (Scorearea), as well as the lung level (ScoreLlevel). The radiograph will be detected as abnormal if any of the score is '1'. Total numbers of classified normal and with disease radiographs are 177 and 35 respectively. From the results at the image level, 78% and 100% of the infection and fluid images are correctly detected as abnormal.

Original languageEnglish
Title of host publicationProceedings - 2015 International Electronics Symposium: Emerging Technology in Electronic and Information, IES 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages62-67
Number of pages6
ISBN (Print)9781467393454
DOIs
Publication statusPublished - 12 Jan 2016
Event17th International Electronics Symposium, IES 2015 - Surabaya, Indonesia
Duration: 29 Sep 201530 Sep 2015

Other

Other17th International Electronics Symposium, IES 2015
CountryIndonesia
CitySurabaya
Period29/9/1530/9/15

Fingerprint

Fluids

Keywords

  • chest radiograph
  • chest x-ray
  • costophrenic angle
  • lung detection

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications

Cite this

Ahmad, W. S. H. M. W., Fauzi, M. F. A., & Wan Zaki, W. M. D. (2016). Abnormality detection for infection and fluid cases in chest radiograph. In Proceedings - 2015 International Electronics Symposium: Emerging Technology in Electronic and Information, IES 2015 (pp. 62-67). [7380815] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ELECSYM.2015.7380815

Abnormality detection for infection and fluid cases in chest radiograph. / Ahmad, Wan Siti Halimatul Munirah Wan; Fauzi, Mohammad Faizal Ahmad; Wan Zaki, Wan Mimi Diyana.

Proceedings - 2015 International Electronics Symposium: Emerging Technology in Electronic and Information, IES 2015. Institute of Electrical and Electronics Engineers Inc., 2016. p. 62-67 7380815.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Ahmad, WSHMW, Fauzi, MFA & Wan Zaki, WMD 2016, Abnormality detection for infection and fluid cases in chest radiograph. in Proceedings - 2015 International Electronics Symposium: Emerging Technology in Electronic and Information, IES 2015., 7380815, Institute of Electrical and Electronics Engineers Inc., pp. 62-67, 17th International Electronics Symposium, IES 2015, Surabaya, Indonesia, 29/9/15. https://doi.org/10.1109/ELECSYM.2015.7380815
Ahmad WSHMW, Fauzi MFA, Wan Zaki WMD. Abnormality detection for infection and fluid cases in chest radiograph. In Proceedings - 2015 International Electronics Symposium: Emerging Technology in Electronic and Information, IES 2015. Institute of Electrical and Electronics Engineers Inc. 2016. p. 62-67. 7380815 https://doi.org/10.1109/ELECSYM.2015.7380815
Ahmad, Wan Siti Halimatul Munirah Wan ; Fauzi, Mohammad Faizal Ahmad ; Wan Zaki, Wan Mimi Diyana. / Abnormality detection for infection and fluid cases in chest radiograph. Proceedings - 2015 International Electronics Symposium: Emerging Technology in Electronic and Information, IES 2015. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 62-67
@inproceedings{2e4d7a538c2e4700b742936f9844511a,
title = "Abnormality detection for infection and fluid cases in chest radiograph",
abstract = "This paper presents an automated abnormality detection system for infection and fluid cases in the lung field for chest radiograph. The abnormality features represented as abnormality scores are investigated based on the sharpness of costophrenic angle (Scoreθn), symmetrical lung area (ScoreLp), area of the lung (Scorearea), as well as the lung level (ScoreLlevel). The radiograph will be detected as abnormal if any of the score is '1'. Total numbers of classified normal and with disease radiographs are 177 and 35 respectively. From the results at the image level, 78{\%} and 100{\%} of the infection and fluid images are correctly detected as abnormal.",
keywords = "chest radiograph, chest x-ray, costophrenic angle, lung detection",
author = "Ahmad, {Wan Siti Halimatul Munirah Wan} and Fauzi, {Mohammad Faizal Ahmad} and {Wan Zaki}, {Wan Mimi Diyana}",
year = "2016",
month = "1",
day = "12",
doi = "10.1109/ELECSYM.2015.7380815",
language = "English",
isbn = "9781467393454",
pages = "62--67",
booktitle = "Proceedings - 2015 International Electronics Symposium: Emerging Technology in Electronic and Information, IES 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - GEN

T1 - Abnormality detection for infection and fluid cases in chest radiograph

AU - Ahmad, Wan Siti Halimatul Munirah Wan

AU - Fauzi, Mohammad Faizal Ahmad

AU - Wan Zaki, Wan Mimi Diyana

PY - 2016/1/12

Y1 - 2016/1/12

N2 - This paper presents an automated abnormality detection system for infection and fluid cases in the lung field for chest radiograph. The abnormality features represented as abnormality scores are investigated based on the sharpness of costophrenic angle (Scoreθn), symmetrical lung area (ScoreLp), area of the lung (Scorearea), as well as the lung level (ScoreLlevel). The radiograph will be detected as abnormal if any of the score is '1'. Total numbers of classified normal and with disease radiographs are 177 and 35 respectively. From the results at the image level, 78% and 100% of the infection and fluid images are correctly detected as abnormal.

AB - This paper presents an automated abnormality detection system for infection and fluid cases in the lung field for chest radiograph. The abnormality features represented as abnormality scores are investigated based on the sharpness of costophrenic angle (Scoreθn), symmetrical lung area (ScoreLp), area of the lung (Scorearea), as well as the lung level (ScoreLlevel). The radiograph will be detected as abnormal if any of the score is '1'. Total numbers of classified normal and with disease radiographs are 177 and 35 respectively. From the results at the image level, 78% and 100% of the infection and fluid images are correctly detected as abnormal.

KW - chest radiograph

KW - chest x-ray

KW - costophrenic angle

KW - lung detection

UR - http://www.scopus.com/inward/record.url?scp=84970015730&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84970015730&partnerID=8YFLogxK

U2 - 10.1109/ELECSYM.2015.7380815

DO - 10.1109/ELECSYM.2015.7380815

M3 - Conference contribution

AN - SCOPUS:84970015730

SN - 9781467393454

SP - 62

EP - 67

BT - Proceedings - 2015 International Electronics Symposium: Emerging Technology in Electronic and Information, IES 2015

PB - Institute of Electrical and Electronics Engineers Inc.

ER -