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A SSLBP-based feature extraction framework to detect bones from knee MRI scans

Published: 09 October 2018 Publication History

Abstract

The medical industry is currently working on a fully autonomous surgical system, which is considered a novel modality to go beyond technical limitations of conventional surgery. In order to apply an autonomous surgical system to knees, one of the primarily responsible areas for supporting the total weight of human body, accurate segmentation of bones from knee Magnetic Resonance Imaging (MRI) scans plays a crucial role. In this paper, we propose employing the Scale Space Local Binary Pattern (SSLBP) feature extraction, a variant of local binary pattern extractions, for detecting bones from knee images. The experimental results demonstrate that the proposed method has an average accuracy rate of 96.10% with an average MCC rate of 88.26%, which significantly outperforms existing intensity-based methods such as fuzzy c-means clustering and deep feature extraction method.

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Cited By

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  • (2023)A Simplified Method of Detection and Predicting the Severity of Knee Osteoarthritis2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)10.1109/ICCCNT56998.2023.10306649(1-7)Online publication date: 6-Jul-2023
  • (2022)Recognition of Knee Osteoarthritis (KOA) Using YOLOv2 and Classification Based on Convolutional Neural NetworkLife10.3390/life1208112612:8(1126)Online publication date: 27-Jul-2022
  • (2022)A Concise Review on Deep Learning for Musculoskeletal X-ray Images2022 International Conference on Digital Image Computing: Techniques and Applications (DICTA)10.1109/DICTA56598.2022.10034618(1-8)Online publication date: 30-Nov-2022
  • Show More Cited By

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cover image ACM Conferences
RACS '18: Proceedings of the 2018 Conference on Research in Adaptive and Convergent Systems
October 2018
355 pages
ISBN:9781450358859
DOI:10.1145/3264746
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]

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Publication History

Published: 09 October 2018

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Author Tags

  1. classification
  2. feature extraction
  3. image processing
  4. image segmentation
  5. knee bone detection
  6. local binary pattern
  7. magnetic resource imaging
  8. segmentation
  9. support vector machine

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  • Research-article

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  • Korean Government

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RACS '18
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Cited By

View all
  • (2023)A Simplified Method of Detection and Predicting the Severity of Knee Osteoarthritis2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)10.1109/ICCCNT56998.2023.10306649(1-7)Online publication date: 6-Jul-2023
  • (2022)Recognition of Knee Osteoarthritis (KOA) Using YOLOv2 and Classification Based on Convolutional Neural NetworkLife10.3390/life1208112612:8(1126)Online publication date: 27-Jul-2022
  • (2022)A Concise Review on Deep Learning for Musculoskeletal X-ray Images2022 International Conference on Digital Image Computing: Techniques and Applications (DICTA)10.1109/DICTA56598.2022.10034618(1-8)Online publication date: 30-Nov-2022
  • (2020)A Comparative Systematic Literature Review on Knee Bone Reports from MRI, X-Rays and CT Scans Using Deep Learning and Machine Learning MethodologiesDiagnostics10.3390/diagnostics1008051810:8(518)Online publication date: 26-Jul-2020

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