CLASSIFICATION OF BIOMEDICAL IMAGES USING CONTENT BASED IMAGE RETRIEVAL SYSTEMS

  • Yinghui Zhang University of California, Berkeley, USA
  • Fengyuan Zhang Northeastern University, China
  • Yantong Cui Anshan No.1 High School, China
  • Ruoci Ning Marian High School, USA
Keywords: Biomedical Images, Content-Based Image Retrieval (CBIR), Gray-Level Co-Occurrence Matrix

Abstract

Because of the numerous application of Content-based image retrieval (CBIR) system in various areas, it has always remained a topic of keen interest by the researchers. Fetching of the most similar image from the complete repository by comparing it to the input image in the minimum span of time is the main task of the CBIR. The purpose of the CBIR can vary from different types of requirements like a diagnosis of the illness by the physician, crime investigation, product recommendation by the e-commerce companies, etc. In the present work, CBIR is used for finding the similar patients having Breast cancer. Gray-Level Co-Occurrence Matrix along with histogram and correlation coefficient is used for creating CBIR system. Comparing the images of the area of interest of a present patient with the complete series of the image of a past patient can help in early diagnosis of the disease. CBIR is so much effective that even when the symptoms are not shown by the body the disease can be diagnosed from the sample images.

Downloads

Download data is not yet available.

References

M. Madugunki, D. S. Bormane, S. Bhadoria and C. G. Dethe, "Comparison of different CBIR techniques," 2011 3rd International Conference on Electronics Computer Technology, Kanyakumari, 2011, pp. 372-375.]. DOI: https://doi.org/10.1109/ICECTECH.2011.5941923

"Histogram of image data," Math Works, [Online]. Available: [Accessed 18 Februrary 2018] https://in.mathworks.com/help/images/ref/imhist.html#buo3qek-2_1]

"Texture Analysis Using the Gray-Level Co-Occurrence Matrix," Math Works, [Online]. Available: [Accessed 18 Februrary 2018] https://in.mathworks.com/help/images/texture-analysis-using-the-gray-level-co-occurrencematrix-glcm.html.

"2-D correlation coefficient," [Online]. Available: [Accessed 18 Februrary 2018] https://in.mathworks.com/help/images/ref/corr2.html.

K. Trojacanec, I. Dimitrovski and S. Loskovska, "Content based image retrieval in medical applications: an improvement of the two-level architecture," IEEE EUROCON 2009, St.- Petersburg, 2009, pp. 118-121. DOI: https://doi.org/10.1109/EURCON.2009.5167614

Rani, Deepu, and Monica Goyal. "A Research Paper on Content Based Image Retrieval System using Improved SVM Technique." International Journal of Engineering and Computer Science 3.12 (2014).

Choudhary, Roshi, et al. "An integrated approach to content-based image retrieval." Advances in Computing, Communications and Informatics (ICACCI, 2014 International Conference on. IEEE, 2014. DOI: https://doi.org/10.1109/ICACCI.2014.6968394

Trojacanec, Katarina, Ivica Dimitrovski, and SuzanaLoskovska. "Content based image retrieval in medical applications: an improvement of the two-level architecture." EUROCON 2009, EUROCON'09. IEEE. IEEE, 2009. DOI: https://doi.org/10.1109/EURCON.2009.5167614

Antani, Sameer K., L. Rodney Long, and George R. Thoma. "Content-based image retrieval for large biomedical image archives." Medinfo. 2004.

Khan, Sumaira Muhammad Hayat, Ayyaz Hussain, and Imad Fakhri Taha Alshaikhli. "Comparative study on content-based image retrieval (CBIR)." Advanced Computer Science Applications and Technologies (ACSAT), 2012 International Conference on. IEEE, 2012. DOI: https://doi.org/10.1109/ACSAT.2012.40

Aloni, Sukhada. "Content Based Image Retrieval in Biomedical Images Using SVM Classification with Relevance Feedback." International Journal of Scientific and Research Publications 3.11 (2002): 1-7.

Ramos, José, et al. "Content-based image retrieval by metric learning from radiology reports: Application to interstitial lung diseases." IEEE journal of biomedical and health informatics20.1 (2016): 281-292. DOI: https://doi.org/10.1109/JBHI.2014.2375491

"National Biomedical Imaging Archive," [Online]. Available: [Accessed 18 Februrary 2018] https://imaging.nci.nih.gov/ncia/login.jsf.

Bagga, Sachin, Akshay Girdhar, and Munesh Chandra Trivedi. "SPMD based time sharing intelligent approach for image denoising." Journal of Intelligent & Fuzzy Systems 32.5 (2017): 3561-3573. DOI: https://doi.org/10.3233/JIFS-169292

Kaur, Manmeet, Akshay Girdhar, and Sachin Bagga. "Cluster Based Approach to CacheOblivious Average Filter Using RMI." The International Symposium on Intelligent Systems Technologies and Applications. Springer International Publishing, 2016. DOI: https://doi.org/10.1007/978-3-319-47952-1_31

Bagga, Sachin, et al. "RMI Approach to Cluster Based Cache Oblivious Peano Curves." Computational Intelligence & Communication Technology (CICT), 2016 Second International Conference on. IEEE, 2016 DOI: https://doi.org/10.1109/CICT.2016.26

Kaur, Gurpinder, Sachin Bagga, and Kulvinder Singh Mann. "Hadoop Approach to Cluster Based Cache Oblivious Peano Curves." Advance Computing Conference (IACC), 2017 IEEE 7th International. IEEE, 2017. DOI: https://doi.org/10.1109/IACC.2017.0037

Published
2018-02-28
How to Cite
Zhang, Y., Zhang, F., Cui, Y., & Ning, R. (2018). CLASSIFICATION OF BIOMEDICAL IMAGES USING CONTENT BASED IMAGE RETRIEVAL SYSTEMS. International Journal of Engineering Technologies and Management Research, 5(2), 181-189. https://doi.org/10.29121/ijetmr.v5.i2.2018.161