No Access

A FUZZY FRAMEWORK FOR CONTENT BASED MAGNETIC RESONANCE IMAGES RETRIEVAL USING SALIENCY MAP

Mana Tarjoman

Mana Tarjoman

Department of Engineering, Abhar Branch, Islamic Azad University, Abhar, Iran

E-mail Address: manatarjoman@gmail.com

Corresponding author

Search for more papers by this author

https://doi.org/10.4015/S1016237217500338Cited by:0 (Source: Crossref)

Abstract

Content-based image retrieval (CBIR) has turned into an important research field with the advancement in multimedia and imaging technology. The term CBIR has been widely used to describe the process of retrieving desired images from a large collection on the basis of features such as color, texture and shape that can be automatically extracted from the images themselves. Considering the gap between low-level image features and the high-level semantic concepts in the CBIR, we proposed an image retrieval system for brain magnetic resonance images based on saliency map. First, the proposed approach exploits the ant colony optimization (ACO) technique to measure the image’s saliency through ants’ movements on the image. The textural features are then calculated from the saliency map of the images. The image retrieval of the proposed CBIR system is based on textural features and the fuzzy approach using Adaptive neuro-fuzzy inference system (ANFIS). Regarding the various categories of images in a database, we define some membership functions in the ANFIS output in order to determine the membership values of the images related to the existing categories. In online image retrieval, a query image is introduced to the system and the relevant images can be retrieved based on query membership values into different classes including normal or tumoral. The experimental results indicate that the proposed method is reliable and has high image retrieval efficiency compared with the previous works.

Keywords:

References

1. Smeulders AWM, Worring M, Santini S, Gupta A, Jain V , Content-based image retrieval at the end of the years, IEEE Trans Pattern Anal Mach Intell 22 :1349, 2000. Crossref, Web of Science, Google Scholar
2. Sinha U, Kangarloo H , Principal component analysis for content-based image retrieval, Radiographics 22 :1272, 2002. Crossref, Web of Science, Google Scholar
3. Muller H, Michoux N, Bandon D, Geissbuhler A , A review of content-based image retrieval systems in medical applications — clinical benefits and future directions, Int J Med Informatics, 2004. Crossref, Web of Science, Google Scholar
4. Chen Y, Wang JZ, Krovetz R , An unsupervised learning approach to content-based image retrieval, IEEE Proc. Int. Symp. Signal Processing and Its Applications, 2003. Google Scholar
5. Vailaya A, Figueiredo MAT, Jain AK, Zhang HJ , Image classification for content-based indexing, IEEE Trans Image Process 10 :117, 2001. Crossref, Web of Science, Google Scholar
6. Han J, Kuang K , Fuzzy color histogram and its use in color image retrieval, IEEE Trans Image Process 11 :944, 2000. Crossref, Web of Science, Google Scholar
7. Shi J, Malik J , Normalized cuts and image segmentation, IEEE Trans Pattern Anal Mach Intell 22 :888, 2000. Crossref, Web of Science, Google Scholar
8. Fernandez J, Aranda J , Image segmentation combining region depth and object features, 15th Int. Conf. Pattern Recognition 1, 2000. Google Scholar
9. Sheikholeslami Gh, Chang W, Zhang A , Semantic clustering and querying on heterogeneous features for visual data, IEEE Trans Knowl Data Eng 14 :988, 2002. Crossref, Web of Science, Google Scholar
10. Sang S, Liu M, Liu J, An Q , A new approach for texture classification in CBIR, Int J Computer Appl Technol 38 :34, 2010. Crossref, Google Scholar
11. Reddy PVN, Prasad KS , Multi wavelet based texture features for content-based image retrieval, Int J Power Control Signal Comput 1, 2010. Google Scholar
12. Hafner J, Sawhney HS, Equits W, Flickner M, Niblack W , Efficient color histogram indexing for quadratic form distance functions, IEEE Trans Pattern Anal Mach Intell 17, 1995. Crossref, Web of Science, Google Scholar
13. Smith JR, Chang SF , Automated image retrieval using color and texture, IEEE Trans Pattern Anal Mach Intell, 1995. Web of Science, Google Scholar
14. Dahshan AE, Salem M, Younis TH , A hybrid technique for automatic MRI brain images classification, Informatica, 2009. Google Scholar
15. Epischina J, de Albuquerque A, Deserno TM , Content-based image retrieval applied to BI-RADS tissue classification in screening mammography, World J Radiol 28 :24, 2011. Google Scholar
16. Rafi Nazari M, Fatemizadeh E , A CBIR system for human brain magnetic resonance image indexing, Int J Computer Appl 7 :33, 2010. Google Scholar
17. Tarjoman M, Fatemizadeh E, Badie K , An implementation of a CBIR System based on SVM learning scheme, J Med Eng Technol 37, 2013. Crossref, Google Scholar
18. Jayech Kh, Mahjoub MA , New approach using Bayesian network to improve content based image classification systems, Int J Computer Sci Issues 7, 2010. Google Scholar
19. Chaplot A, Patnaik LM, Jagannathan NR , Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network, Biomed Signal Process Control 1 :86, 2006. Crossref, Web of Science, Google Scholar
20. Maitra M, Chatterjee A , Hybrid multi resolution Slantlet transform and fuzzy c-means clustering approach for normal-pathological brain MR image segregation, Med Eng Phys, 2007. Web of Science, Google Scholar
21. Kannan A, Mohan V, Anbazhagan N , Image clustering and retrieval using image mining techniques, IEEE Int Conf Computational Intelligence and Computing Research, 2010. Google Scholar
22. Zakariya SM, Ali R, Ahmad N , Unsupervised content based image retrieval by combining visual features of an image with a threshold, Int Conf Computer Technology, 2010. Google Scholar
23. Tian Q, Wu Y, Huang T , Combine user defined region-of-interest and spatial layout for image retrieval, Proc. IEEE Conf. Image Process 3 :746, 2000. Crossref, Google Scholar
24. Rahmani R, Goldman S, Zhang H, Cholleti Sh R, Fritts J , Localized content-based image retrieval, IEEE Trans Pattern Anal Mach Intell, 2008. Crossref, Web of Science, Google Scholar
25. Wang J, Li J, Wiederhold G , Simplicity: Semantics-sensitive integrated matching for picture libraries, IEEE Trans Pattern Anal Mach Intell, 2001. Crossref, Web of Science, Google Scholar
26. Ma L, Tian J, Yu W , Visual saliency detection in image using ant colony optimization and local phase coherence, Electron Lett 46, 2010. Crossref, Web of Science, Google Scholar
27. Dorigo M, Birattari M, Stutzle T, Ant colony optimization, IEEE Comput Intell Mag 1:28, 2006. Google Scholar
28. Tian J, Yu W, Xie S , An ant colony optimization algorithm for image edge detection, Proc. IEEE Congress on Evolutionary Computation, 2008. Google Scholar
29. Haralick RM , Statistical and structural approach to texture. Proc. IEEE 67 :786, 1979. Crossref, Web of Science, Google Scholar
30. Jang JSR , Adaptive-network-based fuzzy inference systems, IEEE Trans Syst Man Cybern 23 :665, 1993. Crossref, Web of Science, Google Scholar
31. www.cma.mgh.harvard.edu/ibsr/ Google Scholar