Framework for Classifying Long Bone Detection Using Image Processing Techniques

Authors

  • Selin Vironicka A Research Scholar, Department of Computer Science, Bishop Heber College (Affiliated to Bharathidasan University), Tiruchirappalli, Tamilnadu, India
  • J.G.R. Sathiaseelan Associate Professor, Department of Computer Science, Bishop Heber College (Affiliated to Bharathidasan University), Tiruchirappalli, Tamilnadu, India

Keywords:

Long Bone Detection, Image Processing Techniques

Abstract

Humans frequently have bone fractures, which can happen as a result of trauma to the bone, simple accidents, osteoarthritis, and bone cancer. Consequently, a critical component of the healthcare profession is the precise detection of bone fractures. In this research, bone fracture analysis is performed using X-ray pictures. The goal of this work is to create a fast and precise method for categorizing bone fractures based on the data obtained from x-ray pictures using image processing. The patients receive pictures of the broken bone, and processing systems such pre-processing, segmentation, edge detection, and feature extraction are used. The final classification of the analyzed pictures into fracture and nonfractured bone will evaluate the precision of various approaches. Our study demonstrates that the suggested strategy is straightforward and effective, making it desirable for ductile fracture identification and classification at a time when doctors and radiologists are interacting with an increasing number of patients and trying to reduce workload. In addition, this method outperforms state-of-the-art methods in positions of outcomes, run response period, and detecting accuracy.

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References

Amirkolaee, Hamed Amini, Dmitry Olegovich Bokov, and Himanshu Sharma. "Development of a GAN architecture based on integrating global and local information for paired and unpaired medical image translation." Expert Systems with Applications 203 (2022): 117421.

Singh, Law Kumar, Hitendra Garg, and Munish Khanna. "Deep learning system applicability for rapid glaucoma prediction from fundus images across various data sets." Evolving Systems (2022): 1-30.

Garg, Hitendra, Neeraj Gupta, Rohit Agrawal, Shivendra Shivani, and Bhisham Sharma. "A real time cloud-based framework for glaucoma screening using EfficientNet." Multimedia Tools and Applications (2022): 1-22.

Yu, Yang, Maria Rashidi, Bijan Samali, Masoud Mohammadi, Thuc N. Nguyen, and Xinxiu Zhou. "Crack detection of concrete structures using deep convolutional neural networks optimized by enhanced chicken swarm algorithm." Structural Health Monitoring (2022): 14759217211053546.

Lu, Shuzhen, Shengsheng Wang, and Guangyao Wang. "Automated universal fractures detection in X-ray images based on deep learning approach." Multimedia Tools and Applications (2022): 1-17.

Guan, Bin, Guoshan Zhang, Jinkun Yao, Xinbo Wang, and Mengxuan Wang. "Arm fracture detection in X-rays based on improved deep convolutional neural network." Computers & Electrical Engineering 81 (2020): 106530.

Basha, Cmak Zeelan, M. Ravi Kishore Reddy, K. Hemanth Sai Nikhil, P. S. M. Venkatesh, and A. V. Asish. "Enhanced computer aided bone fracture detection employing X-ray images by Harris Corner technique." In 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC), pp. 991-995. IEEE, 2020.

Al-Shayea, Tamara K., Constandinos X. Mavromoustakis, Jordi Mongay Batalla, George Mastorakis, Mithun Mukherjee, and Evangelos Pallis. "A Novel Gaussian in Denoising Medical Images with Different Wavelets for Internet of Things Devices." In GLOBECOM 2020-2020 IEEE Global Communications Conference, pp. 1-6. IEEE, 2020.

Dian Li, Cheng Wu and Yiming Wang, “A Novel Iris Texture Extraction Scheme for Iris Presentation Attack Detection”, Journal of Image and Graphics, Vol. 9, No. 3, pp.1-12, 2021.

Modiya, P., & Vahora, S. (2022). Brain Tumor Detection Using Transfer Learning with Dimensionality Reduction Method. International Journal of Intelligent Systems and Applications in Engineering, 10(2), 201–206. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/1310

Lin, Xian, Zengqiang Yan, Zhuo Kuang, Hang Zhang, Xianbo Deng, and Li Yu. "Fracture R‐CNN: An anchor‐efficient anti‐interference framework for skull fracture detection in CT images." Medical Physics (2022).

Kitamura, Gene, Chul Y. Chung, and Barry E. Moore. "Ankle fracture detection utilizing a convolutional neural network ensemble implemented with a small sample, de novo training, and multiview incorporation." Journal of digital imaging 32, no. 4 (2019): 672-677.

Yang, Lv, Shan Gao, Pengfei Li, Jiancheng Shi, and Fang Zhou. "Recognition and Segmentation of Individual Bone Fragments with a Deep Learning Approach in CT Scans of Complex Intertrochanteric Fractures: A Retrospective Study." Journal of Digital Imaging (2022): 1-9.

Haitaamar, Zineddine N., and Nidhal Abdulaziz. "Detection and Semantic Segmentation of Rib Fractures using a Convolutional Neural Network Approach." In 2021 IEEE Region 10 Symposium (TENSYMP), pp. 1-4. IEEE, 2021.

Nguyen, Hoai Phuong, Thi Phuong Hoang, and Huy Hoang Nguyen. "A deep learning based fracture detection in arm bone X-ray images." In 2021 International Conference on Multimedia Analysis and Pattern Recognition (MAPR), pp. 1-6. IEEE, 2021.

Alzaid, Asma, Alice Wignall, Sanja Dogramadzi, Hemant Pandit, and Sheng Quan Xie. "Automatic detection and classification of peri-prosthetic femur fracture." International Journal of Computer Assisted Radiology and Surgery 17, no. 4 (2022): 649-660.

Hardalaç, Fırat, Fatih Uysal, Ozan Peker, Murat Çiçeklidağ, Tolga Tolunay, Nil Tokgöz, Uğurhan Kutbay, Boran Demirciler, and Fatih Mert. "Fracture Detection in Wrist X-ray Images Using Deep Learning-Based Object Detection Models." Sensors 22, no. 3 (2022): 1285.

Varoquaux, Gaël, and Veronika Cheplygina. "Machine learning for medical imaging: methodological failures and recommendations for the future." NPJ digital medicine 5, no. 1 (2022): 1-8.

Yang, Lv, Shan Gao, Pengfei Li, Jiancheng Shi, and Fang Zhou. "Recognition and Segmentation of Individual Bone Fragments with a Deep Learning Approach in CT Scans of Complex Intertrochanteric Fractures: A Retrospective Study." Journal of Digital Imaging (2022): 1-9.

Nguyen, Hoai Phuong, Thi Phuong Hoang, and Huy Hoang Nguyen. "A deep learning based fracture detection in arm bone X-ray images." In 2021 International Conference on Multimedia Analysis and Pattern Recognition (MAPR), pp. 1-6. IEEE, 2021.

Haitaamar, Zineddine N., and Nidhal Abdulaziz. "Detection and Semantic Segmentation of Rib Fractures using a Convolutional Neural Network Approach." In 2021 IEEE Region 10 Symposium (TENSYMP), pp. 1-4. IEEE, 2021

M. J. Traum, J. Fiorentine. (2021). Rapid Evaluation On-Line Assessment of Student Learning Gains for Just-In-Time Course Modification. Journal of Online Engineering Education, 12(1), 06–13. Retrieved from http://onlineengineeringeducation.com/index.php/joee/article/view/45

Wang, Mengxuan, Guoshan Zhang, Bin Guan, Mingyang Xia, and Xinbo Wang. "Multiple Reception Field Network with Attention Module on Bone Fracture Detection Task." In 2021 40th Chinese Control Conference (CCC), pp. 7998-8003. IEEE, 2021.

Ma, Yangling, and Yixin Luo. "Bone fracture detection through the two-stage system of Crack-Sensitive Convolutional Neural Network." Informatics in Medicine Unlocked 22 (2021): 100452.

Wang, Mengxuan, Jinkun Yao, Guoshan Zhang, Bin Guan, Xinbo Wang, and Yueming Zhang. "ParallelNet: Multiple backbone network for detection tasks on thigh bone fracture." Multimedia Systems 27, no. 6 (2021): 1091-1100.

Luo, Jun, Gene Kitamura, Emine Doganay, Dooman Arefan, and Shandong Wu. "Medical knowledge-guided deep curriculum learning for elbow fracture diagnosis from x-ray images." In Medical Imaging 2021: Computer-Aided Diagnosis, vol. 11597, pp. 247-252. SPIE, 2021.

Meneses, B., E. L. Huamani, M. Yauri-Machaca, J. Meneses-Claudio, and R. Perez-Siguas. “Authentication and Anti-Duplication Security System for Visa and MasterCard Cards”. International Journal on Recent and Innovation Trends in Computing and Communication, vol. 10, no. 7, July 2022, pp. 01-05, doi:10.17762/ijritcc.v10i7.5558.

Beyaz, Salih, Koray Açıcı, and Emre Sümer. "Femoral neck fracture detection in X-ray images using deep learning and genetic algorithm approaches." Joint diseases and related surgery 31, no. 2 (2020): 175.

Hardalaç, Fırat, Fatih Uysal, Ozan Peker, Murat Çiçeklidağ, Tolga Tolunay, Nil Tokgöz, Uğurhan Kutbay, Boran Demirciler, and Fatih Mert. "Fracture Detection in Wrist X-ray Images Using Deep Learning-Based Object Detection Models." Sensors 22, no. 3 (2022): 1285.

Weikert, Thomas, Luca Andre Noordtzij, Jens Bremerich, Bram Stieltjes, Victor Parmar, Joshy Cyriac, Gregor Sommer, and Alexander Walter Sauter. "Assessment of a deep learning algorithm for the detection of rib fractures on whole-body trauma computed tomography." Korean Journal of Radiology 21, no. 7 (2020): 891.

Garg, D. K. . (2022). Understanding the Purpose of Object Detection, Models to Detect Objects, Application Use and Benefits. International Journal on Future Revolution in Computer Science &Amp; Communication Engineering, 8(2), 01–04. https://doi.org/10.17762/ijfrcsce.v8i2.2066

Tanzi, Leonardo, Enrico Vezzetti, Rodrigo Moreno, Alessandro Aprato, Andrea Audisio, and Alessandro Massè. "Hierarchical fracture classification of proximal femur X-Ray images using a multistage Deep Learning approach." European journal of radiology 133 (2020): 109373.

Lotfy, Mayar, Raed M. Shubair, Nassir Navab, and Shadi Albarqouni. "Investigation of focal loss in deep learning models for femur fractures classification." In 2019 International Conference on Electrical and Computing Technologies and Applications (ICECTA), pp. 1-4. IEEE, 2019.

The process flow diagram for identifying bone fractures in CT and X-ray pictures

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Published

15.10.2022

How to Cite

[1]
S. . Vironicka A and J. . Sathiaseelan, “Framework for Classifying Long Bone Detection Using Image Processing Techniques”, Int J Intell Syst Appl Eng, vol. 10, no. 1s, pp. 56–66, Oct. 2022.