Cover Image

An approach for an automatic fracture detection of skull DICOM images based on neighboring pixels

Providing easy access to Picture Archiving and Communications Systems (PACS) of selected image slices based on its diagnosis would be useful to teach medical students and educators and for government policies. This requires a simplified retrieval presenting only the key images to the doctors that ha...

Full description

Saved in:
Bibliographic Details
Main Authors: Abubacker, Nirase Fathima, Azman, Azreen, Azmi Murad, Masrah Azrifah, C. Doraisamy, Shyamala
Format: Conference or Workshop Item
Language:English
Published: IEEE 2013
Online Access:http://psasir.upm.edu.my/id/eprint/41325/1/An%20approach%20for%20an%20automatic%20fracture%20detection%20of%20skull%20DICOM%20images%20based%20on%20neighboring%20pixels.pdf
http://psasir.upm.edu.my/id/eprint/41325/
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.upm.eprints.41325
record_format eprints
spelling my.upm.eprints.413252020-07-21T03:38:13Z http://psasir.upm.edu.my/id/eprint/41325/ An approach for an automatic fracture detection of skull DICOM images based on neighboring pixels Abubacker, Nirase Fathima Azman, Azreen Azmi Murad, Masrah Azrifah C. Doraisamy, Shyamala Providing easy access to Picture Archiving and Communications Systems (PACS) of selected image slices based on its diagnosis would be useful to teach medical students and educators and for government policies. This requires a simplified retrieval presenting only the key images to the doctors that has the diagnosis for every study of interest, thus saving doctors time. An automatic detection of diagnosis will help the radiographers in saving time since they consume a lot of time in the process of detecting skull fractures manually and with the automatic annotation of the pathological terms only to the key slices that has findings/diagnosis of the entire image set an efficient retrieval of specific key slices can be achieved. One important abnormality in the skull is its fracture. The proposed research goal concentrates on the automatic detection of normal and abnormal skull images as a part of our work. This paper presents a simple and fast automatic method to detect skull fracture in Digital Imaging and Communications in Medicine (DICOM) to extract the skull bone using histogram based thresholding and with the neighboring pixel connectivity search to identify the fracture. The experimental results of this approach are reliable with high detection rate. IEEE 2013 Conference or Workshop Item PeerReviewed text en http://psasir.upm.edu.my/id/eprint/41325/1/An%20approach%20for%20an%20automatic%20fracture%20detection%20of%20skull%20DICOM%20images%20based%20on%20neighboring%20pixels.pdf Abubacker, Nirase Fathima and Azman, Azreen and Azmi Murad, Masrah Azrifah and C. Doraisamy, Shyamala (2013) An approach for an automatic fracture detection of skull DICOM images based on neighboring pixels. In: 13th International Conference on Intellient Systems Design and Applications (ISDA 2013), 8-10 Dec. 2013, Selangor, Malaysia. (pp. 177-181). 10.1109/ISDA.2013.6920731
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description Providing easy access to Picture Archiving and Communications Systems (PACS) of selected image slices based on its diagnosis would be useful to teach medical students and educators and for government policies. This requires a simplified retrieval presenting only the key images to the doctors that has the diagnosis for every study of interest, thus saving doctors time. An automatic detection of diagnosis will help the radiographers in saving time since they consume a lot of time in the process of detecting skull fractures manually and with the automatic annotation of the pathological terms only to the key slices that has findings/diagnosis of the entire image set an efficient retrieval of specific key slices can be achieved. One important abnormality in the skull is its fracture. The proposed research goal concentrates on the automatic detection of normal and abnormal skull images as a part of our work. This paper presents a simple and fast automatic method to detect skull fracture in Digital Imaging and Communications in Medicine (DICOM) to extract the skull bone using histogram based thresholding and with the neighboring pixel connectivity search to identify the fracture. The experimental results of this approach are reliable with high detection rate.
format Conference or Workshop Item
author Abubacker, Nirase Fathima
Azman, Azreen
Azmi Murad, Masrah Azrifah
C. Doraisamy, Shyamala
spellingShingle Abubacker, Nirase Fathima
Azman, Azreen
Azmi Murad, Masrah Azrifah
C. Doraisamy, Shyamala
An approach for an automatic fracture detection of skull DICOM images based on neighboring pixels
author_facet Abubacker, Nirase Fathima
Azman, Azreen
Azmi Murad, Masrah Azrifah
C. Doraisamy, Shyamala
author_sort Abubacker, Nirase Fathima
title An approach for an automatic fracture detection of skull DICOM images based on neighboring pixels
title_short An approach for an automatic fracture detection of skull DICOM images based on neighboring pixels
title_full An approach for an automatic fracture detection of skull DICOM images based on neighboring pixels
title_fullStr An approach for an automatic fracture detection of skull DICOM images based on neighboring pixels
title_full_unstemmed An approach for an automatic fracture detection of skull DICOM images based on neighboring pixels
title_sort approach for an automatic fracture detection of skull dicom images based on neighboring pixels
publisher IEEE
publishDate 2013
url http://psasir.upm.edu.my/id/eprint/41325/1/An%20approach%20for%20an%20automatic%20fracture%20detection%20of%20skull%20DICOM%20images%20based%20on%20neighboring%20pixels.pdf
http://psasir.upm.edu.my/id/eprint/41325/
_version_ 1674067954261557248
score 13.211869

Similar Items