Asif Mahmud
- Co-Director, IPT Wing
Lecturer
Department of Computer Science and Engineering
Independent University, Bangladesh
Conference Paper2016Cited by 5
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
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
Artificial intelligenceComputer sciencePattern recognition (psychology)Scale-invariant feature transformHistogramComputer visionCluster analysisSupport vector machineKernel (algebra)Feature extractionClassifier (UML)Image (mathematics)
CCDS Authors
Amin Ahsan Ali, PhD
- Director, AI/ML Wing
Professor
Department of Computer Science and Engineering
Independent University, Bangladesh
Research Interests
Artificial Intelligence, Machine Learning
Prof. M Ashraful Amin, PhD
- Founder Director, CCDS
- Director, AI/ML, HCI & DS Wings
Professor
+1 more affiliationDepartment of Information Sciences and Technology
George Mason University, USA
Research Interests
Machine Learning, Cognitive & Vision Science, Cybernetics, Surveillance & Security, ICT in Education, Health, & Agriculture, Human-Computer Interaction, Internet of Things, Robotics
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Cited By
- 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.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]
- 3.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]
- 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.Sayeed Islam, Md. Zakir Hossan. (2022). A Review of Available Standard Dataset for Automated Medicine Pill Recognition Technology. 2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT), 9067, 18–21[10.1109/icicict54557.2022.9917640]