CBIR-DSN: integrating clustering and retrieval platforms for disk space narrowing degradation assessment

Aouache Mustapha, Aini Hussain, Wan Siti Halimatul Munirah Wan Ahmad, Wan Mimi Diyana Wan Zaki, Hamzaini Abdul Hamid

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

A system that is capable of assessing spine osteoarthritis conditions which affect a significant portion of the elderly population could be very valuable to radiologists, researchers of arthritis and musculoskeletal diseases, and educators. To this end, there is very limited research published in the literature concerning the degradation assessment of spinal intervertebral disc space narrowing (DSN). Thus, this paper intends to develop a system that focuses on assessing the degradation of disk space narrowing (DSN) to assist in radiologist’s decision-making in the characterization of cervical and lumbar images. A novel experiment based on our previous research (Aouache et al. 2009; Aouache et al. Biomed Eng Online 14(1):6, 2015) was conducted by integrating clustering and retrieval platforms to achieve this objective. Two shape boundary, 9-points, and B-spline have been used as the foundation for DSN model construction using active shape model. The segmented DSNs have then indexed via region and contour-based features descriptor. For better efficiency, clustering using a vocabulary tree model (VTM) is constructed to identify correct DSN cluster and build multi-clusters subsets for faster and robust retrieval research process. Our system achieved an accuracy of average retrieval rate (ARR) more than 90% and 88% for cervical and lumbar data set accordingly. We expect the proposed system will assist in decision-making and uses by radiologists or researchers for further investigation.

Original languageEnglish
JournalMultimedia Tools and Applications
DOIs
Publication statusPublished - 1 Jan 2019

Fingerprint

Degradation
Decision making
Splines
Experiments

Keywords

  • Disk space narrowing
  • DSN retrieval
  • Indexing approach
  • Modeling
  • Spine radiography
  • VTM clustering

ASJC Scopus subject areas

  • Software
  • Media Technology
  • Hardware and Architecture
  • Computer Networks and Communications

Cite this

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title = "CBIR-DSN: integrating clustering and retrieval platforms for disk space narrowing degradation assessment",
abstract = "A system that is capable of assessing spine osteoarthritis conditions which affect a significant portion of the elderly population could be very valuable to radiologists, researchers of arthritis and musculoskeletal diseases, and educators. To this end, there is very limited research published in the literature concerning the degradation assessment of spinal intervertebral disc space narrowing (DSN). Thus, this paper intends to develop a system that focuses on assessing the degradation of disk space narrowing (DSN) to assist in radiologist’s decision-making in the characterization of cervical and lumbar images. A novel experiment based on our previous research (Aouache et al. 2009; Aouache et al. Biomed Eng Online 14(1):6, 2015) was conducted by integrating clustering and retrieval platforms to achieve this objective. Two shape boundary, 9-points, and B-spline have been used as the foundation for DSN model construction using active shape model. The segmented DSNs have then indexed via region and contour-based features descriptor. For better efficiency, clustering using a vocabulary tree model (VTM) is constructed to identify correct DSN cluster and build multi-clusters subsets for faster and robust retrieval research process. Our system achieved an accuracy of average retrieval rate (ARR) more than 90{\%} and 88{\%} for cervical and lumbar data set accordingly. We expect the proposed system will assist in decision-making and uses by radiologists or researchers for further investigation.",
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author = "Aouache Mustapha and Aini Hussain and Ahmad, {Wan Siti Halimatul Munirah Wan} and {Wan Zaki}, {Wan Mimi Diyana} and {Abdul Hamid}, Hamzaini",
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AU - Abdul Hamid, Hamzaini

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AB - A system that is capable of assessing spine osteoarthritis conditions which affect a significant portion of the elderly population could be very valuable to radiologists, researchers of arthritis and musculoskeletal diseases, and educators. To this end, there is very limited research published in the literature concerning the degradation assessment of spinal intervertebral disc space narrowing (DSN). Thus, this paper intends to develop a system that focuses on assessing the degradation of disk space narrowing (DSN) to assist in radiologist’s decision-making in the characterization of cervical and lumbar images. A novel experiment based on our previous research (Aouache et al. 2009; Aouache et al. Biomed Eng Online 14(1):6, 2015) was conducted by integrating clustering and retrieval platforms to achieve this objective. Two shape boundary, 9-points, and B-spline have been used as the foundation for DSN model construction using active shape model. The segmented DSNs have then indexed via region and contour-based features descriptor. For better efficiency, clustering using a vocabulary tree model (VTM) is constructed to identify correct DSN cluster and build multi-clusters subsets for faster and robust retrieval research process. Our system achieved an accuracy of average retrieval rate (ARR) more than 90% and 88% for cervical and lumbar data set accordingly. We expect the proposed system will assist in decision-making and uses by radiologists or researchers for further investigation.

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