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2-Stage classification of knee joint thermograms for rheumatoid arthritis prediction in subclinical inflammation

  • Shawli Bardhan
  • Mrinal Kanti BhowmikEmail author
Technical Paper
  • 17 Downloads

Abstract

Presence of inflammation in knee joint is the early indication of arthritis. In this paper, we performed the inflamed region segmentation from knee joint thermograms for structural feature extraction based knee abnormality prediction. Existing four popular segmentation techniques are investigated, namely K-means, Fuzzy C-means, Otsu, Single seeded region growing. We proposed modified multi-seeded region growing method that generates 98.6% accurate segmentation rate compared to ground truth of inflammation. Based on the spread of the inflammation oriented structural feature analysis, in the first stage of classification we classified arthritis affected knee joint thermograms, and all other types of thermograms (non-arthritis) with 91% accuracy. Among different types of arthritis, the most damaging type that causes disability of joints in long run is known as rheumatoid arthritis (RA). Early diagnosis of RA in subclinical stage enormously helps clinicians to decrease the disease affect. In second stage of classification, we integrated the RA and non-RA categorization by extracting texture, shape and frequency level features. Experiment shows that the combination of all features decreases the accurate detection rate of RA classification. To increase the classification rate, we incorporated the accuracy based feature selection procedure. The RA classification rate obtained with accuracy based feature selection is 73% whereas existing support vector machine-recursive feature elimination (SVM-RFE) and RELIEF methods provide 67% and 71% correct classification rate respectively. The area under the curve (AUC) of accuracy based feature selection, SVM-RFE, and RELIEF for RA classification are 0.72, 0.65 and 0.67, respectively and it indicates better classification outcome of the accuracy based feature selection method.

Keywords

Arthritis RA Segmentation Feature extraction Feature selection Classification 

Notes

Acknowledgements

This work was supported by the Indian Council of Medical Research (ICMR), Government of India, under Grant Number. 5/7/1516/2016-RCH, Dated: 20/06/2017. The second author would like to thank Dr. S. B. Nath, Assistant Professor, Agartala Government Medical College (AGMC), Agartala for his kind support during the collection and inflammation oriented ground truth generation of knee thermogram dataset. Also authors would like to thank Prof. Dipti Prasad Mukherjee, India Statistical Institute, Kolkata for his valuable suggestions regarding feature selection during the classification stage of this work and Prof. Debotosh Bhattacharjee, Jadavpur University, Kolkata for his guidance during the revision stage of the manuscript.

Compliance with ethical standards

Conflict of interest

The author declares no potential conflict of interest with respect to the authorship and/or publication of this article.

Ethical approval

The study was approved by the Research cell and Ethics committee of Agartala Government Medical College, Tripura, India with approval Ref. No. 4(6-11)-AGMC/Medical Education/Ethics Com./2018/15136, Dated, 31st December, 2018.

Informed consent

Informed consent was obtained from all individual participants included in the study.

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Copyright information

© Australasian College of Physical Scientists and Engineers in Medicine 2019

Authors and Affiliations

  1. 1.Computer Science and EngineeringTripura UniversitySuryamaninagarIndia

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