Google scholar arxiv informatics ads IJAIS publications are indexed with Google Scholar, NASA ADS, Informatics et. al.

Call for Paper

-

May Edition 2020
International Journal of Applied Information Systems solicits high quality original research papers for the May 2020 Edition of the journal. The last date of research paper submission is April 15, 2020.

Content based Medical Image Retrieval with SVM Classification and Relevance Feedback

Sujata T Bhairnallykar, V. B. Gaikwad Published in Image Processing

Content based Medical Image Retrieval with SVM Classification and Relevance Feedback
IJAIS Proceedings on International Conference and workshop on Advanced Computing 2013
Year of Publication: 2013
© 2012 by IJAIS Journal
Series icwac Number 4
Authors Sujata T Bhairnallykar, V. B. Gaikwad
10.5120/icwac1318
Download full text

  1. Sujata T Bhairnallykar and V B Gaikwad. Article: Content based Medical Image Retrieval with SVM Classification and Relevance Feedback. IJAIS Proceedings on International Conference and workshop on Advanced Computing 2013 ICWAC(4):25-29, July 2013. BibTeX

    @article{key:article,
	author = "Sujata T Bhairnallykar and V. B. Gaikwad",
	title = "Article: Content based Medical Image Retrieval with SVM Classification and Relevance Feedback",
	journal = "IJAIS Proceedings on International Conference and workshop on Advanced Computing 2013",
	year = 2013,
	volume = "ICWAC",
	number = 4,
	pages = "25-29",
	month = "July",
	note = "Published by Foundation of Computer Science, New York, USA"
}

Abstract

As the network and development of multimedia technologies are becoming more popular, users are not satisfied with the traditional information retrieval techniques. So now a day the content based image retrieval using relevance feedback are becoming a source of exact and fast retrieval. The idea of Content-based Image Retrieval (CBIR) using Relevance Feedback systems is to automatically extract image contents based on image features, i. e. color, texture, and shape and store in database and compare input query image feature with the features stored in database. Relevance feedback is applied to reduce the gap between high-level image semantics and low-level image features. Semantic gap is the difference between human perception of a concept and how it can be represented using machine level language.

Reference

  1. T. C. Wong, Medical Image Databases. New York, LLC: Springer–Verlag, 1998.
  2. H. Muller, N. Michoux, D. Bandon, and Geissbuhler, "A review of content-basedimage retrieval applications—Clinical benefits and future directions," Int. J. Med. Informat. , vol. 73, no. 1, pp. 1–23, 2004.
  3. A. Smeulder, M. Worring, S. Santini, A. Gupta, and R. Jain, "Contentbased image retrieval at the end of the early years," IEEE Trans. Pattern Anal. Machine Intell vol. 22, no. 12, pp. 1349–1380, Dec. 2000.
  4. C. R. Shyu, C. E. Brodley, A. C. Kak, A. Kosaka, A. M Aisen, and L. S. Broderick, "ASSERT: A physician-in-the-loop content-based image retrieval system for HRCT image databases," Comput. Vis. Image Understand. , vol. 75, pp. 111–132, 1999.
  5. W. Hsu, S. Antani, L. R. Long, L. Neve, and G. R. Thoma, "SPIRS: A web-based image retrieval system for large biomedical databases," Int. J. Med. Informat. , vol. 78, pp. 13–24, 2008.
  6. Efficient Relevance Feedback for Content-Based Image Retrieval by Mining User Navigation Patterns -IEEE transactions on knowledge and data engineering, vol. 23, no. 3, march 2011.
  7. Balasubramani R, Dr. V. Kannan "Efficient use of MPEG-7 Color Layout and Edge Histogram Descriptors in CBIR Systems" Global Journal of Computer Science and Technology.
  8. Minyoung Eom, and Yoonsik Choe "Fast Extraction of Edge Histogram in DCT Domain based on MPEG7" World Academy of Science, Engineering and Technology 9 2005
  9. Mahesh Pal "Multiclass Approaches for Support Vector Machine Based Land Cover Classification "Lecturer, Department of Civil engineering National Institute of Technology Kurukshetra, 136119, Haryana (INDIA) mpce_pal@yahoo. co. uk
  10. M. M. Rahman, S. K. Antani, and G. R. Thoma, "A classification-driven similarity matching framework for retrieval of biomedical images," presented at the 11th ACM Int. Conf. Multimedia Inf. Retrieval, National Constitution Center, Philadelphia, Pennsylvania, Mar. 29–31.
  11. A Learning-Based Similarity Fusion and Filtering Approach for Biomedical Image Retrieval Using SVM Classification and Relevance Feedback - IEEE transactions on information technology in biomedicine, vol. 15, no. 4, july 2011.
  12. F Long, H Zhang, DD Feng "Fundamentals of Content-based Image retrieval - Multimedia Information Retrieval and Management 17, 2003- Springer
  13. Pushpa B. PATIL, Manesh B. KOKARE "Relevance Feedback in Content Based Image Retrieval: A Review" Journal of Applied Computer Science & Mathematics, no. 10 (5) /2011, Suceava
  14. Content-Based Image Retrieval: State-of-the-Art and Challenges" Amandeep Khokher et al. / (IJAEST) International Journal of Advanced Engineering Sciences and Technologies Vol No. 9, Issue No. 2, 207 - 211

Keywords

Content-based image retrieval, Relevance feedback, SVM, CLD, EHD