• Skip to primary navigation
  • Skip to main content
  • Skip to primary sidebar
  • Skip to footer

Centre for Artificial Intelligence and Cybersecurity – AIRI

  • Home
  • About Us
    • Vision, Mission and Goals
    • Center Activities
    • Center Faculty
    • Steering Committee
    • Press
  • Research
    • Scientific Projects
    • Research Papers
  • Laboratories
    • Machine Learning
    • Natural Speech & Language Processing
    • Blockchain Technology
    • Information Processing & Pattern Recognition
    • AI in Medicine
    • Data Mining
    • Computer Vision
    • Human-Computer Interaction
    • Maritime Cybersecurity
    • Autonomous Navigation
    • AI in Mechatronics
    • AI in Education
    • Hybrid Computational Methods
    • Drug Design
    • Legal Aspects of AI
    • Ethically Aligned AI
    • Cultural Complexity
  • Collaboration
    • Industry Collaboration
    • Industry Projects
    • International Collaboration
  • News
  • Contact

Local-Entropy Based Approach for X-Ray Image Segmentation and Fracture Detection

28.03.2019

Local-Entropy Based Approach for X-Ray Image Segmentation and Fracture Detection

The paper proposes a segmentation and classification technique for fracture detection in X-ray images. This novel rotation-invariant method introduces the concept of local entropy for de-noising and removing tissue from the analysed X-ray images, followed by an improved procedure for image segmentation and the detection of regions of interest. The proposed local Shannon entropy was calculated for each image pixel using a sliding 2D window. An initial image segmentation was performed on the entropy representation of the original image. Next, a graph theory-based technique was implemented for the purpose of removing false bone contours and improving the edge detection of long bones. Finally, the paper introduces a classification and localisation procedure for fracture detection by tracking the difference between the extracted contour and the estimation of an ideal healthy one. The proposed hybrid method excels at detecting small fractures (which are hard to detect visually by a radiologist) in the ulna and radius bones—common injuries in children. Therefore, it is imperative that a radiologist inspecting the X-ray image receives a warning from the computerised X-ray analysis system, in order to prevent false-negative diagnoses. The proposed method was applied to a data-set containing 860 X-ray images of child radius and ulna bones (642 fracture-free images and 218 images containing fractures). The obtained results showed the efficiency and robustness of the proposed approach, in terms of segmentation quality and classification accuracy and precision (up to 91.16% and 86.22%, respectively).

The research was conducted at the Faculty of Engineering, University of Rijeka (www.riteh.uniri.hr).

Authors:
Franko Hržić, Ivan Štajduhar, Sebastian Tschauner, Erich Sorantin, Jonatan Lerga
Journal:
Entropy
Publishing date:
28.03.2019
View original article Download the paper

Primary Sidebar

Latest Projects

Machine Learning for Knowledge Transfer in Medical Radiology

Estimating River Discharges in Highly Stratified Estuaries

Multilayer Framework for the Information Spreading Characterization in Social Media during the COVID-19 Crisis (InfoCoV)

European Network for assuring food integrity using non-destructive spectral sensors

National Competence Centres in the Framework of EuroHPC (EUROCC)

Latest Research Papers

Rule-Based EEG Classifier Utilizing Local Entropy of Time–Frequency Distributions

Rethinking Effects of Innovation in Competition In The Era of New Digital Technologies

A Comparison of Approaches for Measuring the Semantic Similarity of Short Texts Based on Word Embeddings

Indoor Localization Based on Infrared Angle of Arrival Sensor Network

Gravitational-Wave Burst Signals Denoising Based on the Adaptive Modification of Intersection of Confidence Intervals Rule

Latest News

U Rijeci radi Centar za umjetnu inteligenciju, već su u prvoj godini rada povukli šest milijuna u 14 projekata

UNIRI Excellence Awards in Science

Talk on conference “Exploring Digital Legal Landscapes”

ICAIH 2020 conference presentation

International conference “Exploring Digital Legal Landscapes” – 11th of December, 2020

We provide the expertise for solving real world problems using AI

If your company wants to implement artificial intelligence in your products or services, or increase your level of cybersecurity, our multidisciplinary team of scientists is your ideal partner.

Contact us

Footer

Center for Artificial Intelligence and Cybersecurity
  • jlerga@airi.uniri.hr
  • +385 51 406 500

University of Rijeka

University of Rijeka

About the Center

  • About Us
  • News
  • Privacy Policy
  • Contact

Center Activities

  • Laboratories
  • Scientific Projects
  • Industry Projects
  • Research Papers
  • Industry Collaboration
  • International Collaboration

Footer bottom left

© 2020 Center for Artificial Intelligence and Cybersecurity, all rights reserved.

Designed & developed by Nela Dunato Art & Design