Conference Paper2016Cited by 6

Classifying digital X-ray images into different human body parts

Sanad Saha, Asif Mahmud, Amin Ahsan Ali, Md Ashraful Amin

2016 5th International Conference on Informatics, Electronics and Vision (ICIEV)

IEEE, pp. 67–71

  • Good Health and Well-being — Relevance: 61%
DOI: 10.1109/iciev.2016.7760190URL

Abstract

In medical information retrieval research, automatically classifying X-ray images based on body-parts is a challenging problem. In ImageCLEF's 2015 campaign there was a contest where the participants were challenged to cluster X-ray images into different groups based on presence of particular body-part in that X-ray image. In brief the challenge was to classify given X-ray images primarily into five groups which were: head-neck, body, upper-limb, lower-limb and true-negative. In our approach to solve this task we extracted features from the given images using dense multi-scale SIFT, used Elkan k-means clustering to create visual dictionary of extracted features, Randomized KD-Tree to speed up the Elkan K-Means, Spatial Pyramid Histogram as image descriptors to train Chi-kernel based SVM as classifier. Proposed system is able to classify X-ray images into one of the five classes with 85 percent accuracy.

Keywords

  • X-ray image classification
  • medical image retrieval
  • visual dictionary
  • SIFT features
  • body part classification

CCDS Authors

Asif Mahmud
Asif Mahmud

Asif Mahmud

  • Co-Director, IPT Wing

  • Lecturer

    Department of Computer Science and Engineering

    Independent University, Bangladesh (IUB)

Prof. Amin Ahsan Ali, PhD
Amin Ahsan Ali, PhD

Prof. Amin Ahsan Ali, PhD

  • Director, AI/ML Wing
  • Supervisor, ComBio Wing

  • Professor

    Department of Computer Science and Engineering

    Independent University, Bangladesh (IUB)

Published at

ACL, CHI, UbiComp, ECCV, IJCAI, ECAI, WACV, IPSN, PAKDD, LREC, IJCNN, ICPR, ICIP, BEA, BLP, ICMLA, Scientific Reports, Scientific Data, Pattern Recognition, Information Sciences, Ad Hoc Networks, Drug and Alcohol Dependence, PLOS ONE

Prof. M Ashraful Amin, PhD
M Ashraful Amin, PhD

Prof. M Ashraful Amin, PhD

  • Founder Director, CCDS
  • Director, AI/ML, HCI & DS Wings

Published at

ACL, CHI, UbiComp, ECAI, WACV, PAKDD, IJCNN, ICPR, ICIP, HCII, CLEF, BLP, Scientific Reports, Pattern Recognition, JMIR, IEEE Access, Smart Agricultural Technology, Frontiers in Computational Neuroscience, Diabetes Research and Clinical Practice, PLOS ONE

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Cited By

  1. 1.Zheng Wang, Yu Meng, Futian Weng, Yinghao Chen, Fanggen Lu, Xiaowei Liu, Muzhou Hou, Jie Zhang. (2019). An Effective CNN Method for Fully Automated Segmenting Subcutaneous and Visceral Adipose Tissue on CT Scans. Annals of Biomedical Engineering, 48(1), 312–328[10.1007/s10439-019-02349-3]
  2. 2.Hieu H. Pham, Dung V. Do, Ha Q. Nguyen. (2021). DICOM Imaging Router: An Open Deep Learning Framework for Classification of Body Parts from DICOM X-ray Scans. medRxiv[10.1101/2021.08.13.21261945]
  3. 3.Reeja S R, Sangameswar J, Solomon Joseph Joju, Mrudhul Reddy Gangula, Sujith S. (2024). X-ray body Part Classification Using Custom CNN. EAI Endorsed Transactions on Pervasive Health and Technology, 10[10.4108/eetpht.10.5577]
  4. 4.Raghesh Krishnan K, Padmavathi S. (2025). Body Part Classification from Gabor Enhanced X-Ray Images using Deep Convolutional Models. Procedia Computer Science, 260, 101–109[10.1016/j.procs.2025.03.182]
  5. 5.Marius Popescu, Antoanela Naaji. (2021). Detection of small tumors of the brain using medical imaging. Elsevier eBooks, 33–53[10.1016/b978-0-12-822271-3.00013-x]
  6. 6.Sayeed Islam, Md. Zakir Hossan. (2022). A Review of Available Standard Dataset for Automated Medicine Pill Recognition Technology. International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT), 9067, 18–21[10.1109/icicict54557.2022.9917640]
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