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Research Review on Feature Extraction Methods of Human Being’s X-Ray Image Analysis


Affiliations
1 UCCC & SPBCBA & SDGH College of BCA & IT, (BCA Department), Surat, India
2 Department of Computer Science, Saurashtra University, Rajkot, India
     

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Medical image processing covers various types of images such as tomography, mammography, radiography (X-Ray images), cardiogram, CT scan images etc. X-Ray is a type of image in which electronic radiation is passed in human body to capture images of injured parts. Once the X-Ray image is captured, orthopaedics doctors to detect degenerative conditions, trauma, sports injury, tumors, congenital issues etc. manually diagnose it. In automated medical diagnosis system, image processing has to go through various stages such as image acquisition, enhancement, feature extraction, ROI detection, interpretation etc. Feature extraction is one of the important steps of image processing which mainly focus on detection of the region of interest from the image. It includes various mathematical, statistical and scientific algorithms to detect characteristics from the targeted image to narrow down the image. Many researchers have worked on feature extraction from X-Ray image to contribute into automated X-Ray image processing system. In this research paper, we have presented an extensive research review on “Feature Extraction” step of digital image processing based on X-Ray image of human being.

Keywords

Orthopaedics, Congenital Issues, Trauma, ROI, X-Ray, Feature Extraction, Image Processing.
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  • Bharodiya, A.K., and Gonsai, A.M. (2017). Research Review on human being’s X-Ray image analysis through image processing, Proceedings of National Conference on Sustainable Computing and Information Technology, SCET, Surat, pp. 38-42.

  • Bhowmik, M., Ghoshal, D., and Bhowmik, S. (2015). Automated Medical Image Analyser, IEEE ICCSP 2015 conference, pp. 0974-0978.

  • Gonzalez, R., C., and Woods, R., E. (2008). Digital Image Processing, USA, PE & PH, pp. 1-34

  • Schmidt, C. (2018). 3-D X-Ray Imaging With Nanometer Resolution for Advanced Semiconductor Packaging FA, IEEE TRANSACTIONS ON COMPONENTS, PACKAGING AND MANUFACTURING TECHNOLOGY, Vol. 8 (5), pp.745-749.

  • Mohamed., M. H., and AbdeISamea., M. M. (2008). An Efficient Clustering based Texture Feature Extraction for Medical Image, IEEE Proceedings of International Workshop on Data Mining and Artificial Intelligence, Bangladesh, pp. 88-93.

  • Suzuki K. et al (2005). False-positive Reduction in Computer-aided Diagnostic Scheme for Detecting Nodules in Chest Radiographs by Means of Massive Training Artificial Neural Network, Academic Radiology, Vol. 12 (2), pp. 191-201.

  • Jain, A.K. (1989). Fundamentals of Digital Image Processing, USA, PE & PH, pp. 342-425.

  • Nixon, M. S. and Aguado, A. S. (2012). Feature Extraction & Image Processing for Computer Vision, Third Edition, UK, Elsevier, pp. 137-212.

  • Rogers, L.F., Talianovic, M.S. and Boles, C.A. et al (2008) Grainger & Allison’s Diagnostic Radiology: A Textbook of Medical Imaging, New York, Churchill Livingstone, Chap 46.

  • Giacinto, G., Roli, F., Fumera, G. (2000). Selection of Image Classifiers, IEEE Electronics Letters, Vol. 36(5), pp. 420-422.

  • Zare, M.R., Mueen, A., Awedh, M. et al. ( 2013). Automatic classification of medical X-ray images: hybrid generative-discriminative approach, IEEE IET Image Processing, Vol. 7 (5), pp. 523-532.

  • Akcay, S., Kundegorski, M.E., Chris, G. et al. (2018). Using Deep Convolutional Neural Network Architectures for Object Classification and Detection within X-ray Baggage Security Imagery, IEEE Transactions on Information Forensics and Security, Vol. 13 (9), pp. 2203-2215.

  • Hansen, L.K., Salamon, P. (1990). Neural network ensembles, IEEE Trans., 1990, PAMI-12, (10), pp. 993-1001.

  • Kodogiannisa, V.S., Boulougourab, M., Wadgea, E. and Lygourasc, N. (2007). The usage of soft-computing methodologies in interpreting capsule endoscopy, Elsevier Engineering Applications of Artificial Intelligence, Vol. 20 (4), pp.539-553.

  • Ma, Y. and Wang, Y. (2015). Text Detection in Medical Images Using Local Feature Extraction and Supervised Learning, IEEE 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), pp. 953-958.

  • Shuqi, C., Shen, C. et al (2017). Application of Neural Network Based on SIFT Local Feature Extraction in Medical Image Classification, IEEE 2nd International Conference on Image, Vision and Computing, pp.92-97.

  • Poomimadevi, C. S. and Sulochana, H. C. (2016). Automatic detection of pulmonary tuberculosis using image processing techniques, IEEE WiSPNET conference, pp.798-802.

  • Joykutty, B., Satheeshkumar, K. G. and Samuvel, B. (2016). Automatic Tuberculosis Detection using Adaptive Thresholding in Chest Radiographs, IEEE International Conference on Emerging Technological Trends.

  • Barabas, J., Capka, M., Babusiak, B. et al (2012). Analysis, 3D reconstruction and anatomical feature extraction from medical images, IEEE International Conference on Biomedical Engineering and Biotechnology, pp.731-735.

  • Chaudhary, A. and Sukhraj, S. S. (2012), Lung Cancer Detection on CT Images by using Image Processing, IEEE International Conference on Computing Sciences, pp.142-146.

  • Chen, H. and Huang, Z. H. (2014). Medical Image Feature Extraction and Fusion Algorithm Based on K-SVD, IEEE Ninth International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, pp. 333-337.

  • Ding, Y., Zhao, Y. and Zhao, X. (2017). Image Quality Assessment Based on Multi-Feature Extraction and Synthesis with Support Vector Regression, Elsevier Signal Processing: Image Communication, Vol. 54, pp. 81-92.

  • Litjens, G., Kooi, T., Babak, E. B. et al (2017). A Survey on Deep Learning in Medical Image Analysis, Elsevier Medical Image Analysis, Vol. 42, pp. 60-88.

  • Kaur, B. and Jindal, S. (2014). An implementation of Feature Extraction over medical images on OPEN CV Environment, IEEE International Conference on Devices, Circuits &Communications.

  • Swati, V. S. and Vrushali, G. N. (2011). Design of Feature Extraction in Content Based Image Retrieval (CBIR) using Color and Texture, International Journal of Computer Science & Informatics, Vol-I (II), pp.57-61.

  • Hossein, M. M. and Jacques A. D. (2017). Enhanced X-ray image segmentation method using prior shape, IET Computer Vision, Vol. 11(2), pp. 145-152.

  • Oishila, B., Arindam, B. and Bhargab, B.B. (2016). Long-bone fracture detection in digital X-ray images based on digital-geometric techniques, Elsevier Computer Methods and programs in Biomedicine, Vol. 123, pp.2-14.

  • Seyyed, M. M., Mohammad S. H. et al (2012). Novel Shape Texture Feature Extraction for Medical X-Ray Image Classification, International Journal of Innovative Computing, Information and Control, Vol. 8 (1-B), pp. 659-673.

  • Ratnasari, N. R., Adhi S., Indah, S. et al (2013). Thoracic X-ray Features Extraction using thresholding-based ROI template and PCA-based Features Selection for Lung TB Classification Purposes, IEEE 3rd International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering (ICICI-BME), pp. 65-69.

  • Kazeminia, N., Karimi, S.M., Soroushmehr, R. et al (2015). Region of Interest Extraction for Lossless Compression of Bone X-Ray Images, 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3061-3064.


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  • Research Review on Feature Extraction Methods of Human Being’s X-Ray Image Analysis

Abstract Views: 406  |  PDF Views: 4

Authors

Anil K. Bharodiya
UCCC & SPBCBA & SDGH College of BCA & IT, (BCA Department), Surat, India
Atul M. Gonsai
Department of Computer Science, Saurashtra University, Rajkot, India

Abstract


Medical image processing covers various types of images such as tomography, mammography, radiography (X-Ray images), cardiogram, CT scan images etc. X-Ray is a type of image in which electronic radiation is passed in human body to capture images of injured parts. Once the X-Ray image is captured, orthopaedics doctors to detect degenerative conditions, trauma, sports injury, tumors, congenital issues etc. manually diagnose it. In automated medical diagnosis system, image processing has to go through various stages such as image acquisition, enhancement, feature extraction, ROI detection, interpretation etc. Feature extraction is one of the important steps of image processing which mainly focus on detection of the region of interest from the image. It includes various mathematical, statistical and scientific algorithms to detect characteristics from the targeted image to narrow down the image. Many researchers have worked on feature extraction from X-Ray image to contribute into automated X-Ray image processing system. In this research paper, we have presented an extensive research review on “Feature Extraction” step of digital image processing based on X-Ray image of human being.

Keywords


Orthopaedics, Congenital Issues, Trauma, ROI, X-Ray, Feature Extraction, Image Processing.

References