Hardware Implementation of Bone Fracture Detector Using Fuzzy Method Along with Local Normalization Technique

Abdullah-Al Nahid (), Tariq M. Khan () and Yinan Kong ()
Additional contact information
Abdullah-Al Nahid: Macquarie University
Tariq M. Khan: Macquarie University
Yinan Kong: Macquarie University

Annals of Data Science, 2017, vol. 4, issue 4, No 6, 533-546

Abstract: Abstract Bone fracture detection from the digital image segmentation is a well-known image processing application which is frequently used to process biomedical images. Hardware realization of different image processing algorithm specially utilizing Field Programmable Gate Array (FPGA) has been gained a great interest among the researchers. FPGA has many significant features like spatial and temporal parallelism that best suits for real-time implementation of image processing. To gain the benefit from these characteristics of a FPGA, a new method for bone fracture detection is proposed and its performance is validated through real-time implementation. Simulation results show that the proposed method give superior performance than the existing method.

Keywords: Bone fracture; Canny; Fuzzy; FPGA; Normalization (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s40745-017-0118-z Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:aodasc:v:4:y:2017:i:4:d:10.1007_s40745-017-0118-z

Ordering information: This journal article can be ordered from
https://www.springer ... gement/journal/40745

DOI: 10.1007/s40745-017-0118-z

Access Statistics for this article

Annals of Data Science is currently edited by Yong Shi

More articles in Annals of Data Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().