An Efficient Salient Feature based Histology Image Retrieval


(*) Corresponding author


Authors' affiliations


DOI's assignment:
the author of the article can submit here a request for assignment of a DOI number to this resource!
Cost of the service: euros 10,00 (for a DOI)

Abstract


Due to the development of digital microscopy technologies there are huge quantity of histology images are collected and stored in database. The capability to handle and access these collections of histology images is considered as a key constituent for next generation of medical systems. These types of image retrieval system are the combination of different features play major key responsibility to retrieve images. It aims at attractive the expressive authority of visual and salient features equivalent to semantically consequential queries.  Most of the existing work doesn’t  go behind with different combination of features to retrieve digital images along with salient features and visual feature still becomes major problem, extraction of these features also not major problem .To overcome  these issue in this paper presents a improved Gaussian mixture model that routinely extracts both salient and visual representation of features from histology image database . A Gabor filtering framework is proposed, which makes the salient regions further radiant in the saliency maps to retrieve histology images with enhanced image quality results. The major objective of the work is to found the most significant visual representation of features and salient features for a specific keyword that is connected with numerous query image retrieval processes for exact matching of results. The proposed work uses an Enhanced Artificial Fish Swarm Algorithm (AFSA) method, which aims to appreciate an best possible visual-semantic matching purpose by together allowing for the different preference of the collection of query images.  It is capable to discover different types of features that summarize diverse portions of the majority representative of visual patterns and salient features for every conception. Experimental results demonstrate that proposed EAFSA system for content-based histology image retrieval system, high retrieval accurateness other than existing methods.
Copyright © 2014 Praise Worthy Prize - All rights reserved.

Keywords


Content Based Image Retrieval (CBIR); Histology Image Retrieval; Gaussian Mixture Model; Gabor Filtering; Enhanced Artificial Fish Swarm Algorithm (EAFSA) Optimization

Full Text:

PDF


References


Gonzalez FA, Romero E. Biomedical Image Analysis and Machine Learning Technologies: Application and Techniques, 1st ed., Information Science Reference - Imprint of: IGI Publishing, Hershey, PA; 2009. Ch. 1. From Biomedical Image Analysis to Biomedical Image Understanding Using Machine Learning.

H. M¨uller, N. Michoux, D. Bandon, and A. Geissbuhler, “A review of content-based image retrieval systems in medical applications–Clinical benefits and future directions,” Int. J. Med. Inf., vol. 73, no. 1, pp. 1–23, Feb. 2004.

Vimaladevi, M., Kalaavathi, B., Microarray gene ranking technique based on modified successive feature selection algorithm, (2014) International Review on Computers and Software (IRECOS), 9 (3), pp. 592-598.

V.S. Tseng, J.H. Su, B.W. Wang, and Y.M. Lin, “Web Image Annotation by Fusing Visual Features and Textual Information,” Proc. 22nd ACM Symp. Applied Computing, Mar. 2007.

J. Jeon, V. Lavrenko, and R. Manmatha, “Automatic Image Annotation and Retrieval Using Cross-Media Relevance Models,” Proc. 26th Annual International ACM SIGIR Conf., pp. 119-126, 2003.

Haridas, K., Selvadoss Thanamani, A., An efficient image clustering and content based image retrieval using fuzzy K means clustering algorithm, (2014) International Review on Computers and Software (IRECOS), 9 (1), pp. 147-153.

A. W. Smeulders, M.Worring, S. Santini, A. Gupta, and R. Jain, “Content- based image retrieval at the end of the early years,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 12, pp. 1349–1380, Dec. 2000.

Muller H, Michoux N, Bandon D, Geissbuhler A . A review of content-base Image retrieval systems in medical applications-clinical benefits and future directions International journal of medical informatics 2004;73:1–23

Zheng L, Wetzel AW, Gilbertson J, Becich MJ,” Design and analysis of contentbased pathology image retrieval system”, IEEE Transactions on InformationTechnology in Biomedicine 2003;7(4):249–55.

Neetu Sharma. S1, Paresh Rawat . S2 and jaikaran Singh.S, “Efficient CBIR Using Color Histogram Processing,” Signal & Image Processing: An International Journal (SIPIJ) Vol.2, No.1, March 2011, DOI : 0.5121/sipij.2011.2108.

Majid Fakheri, Tohid Sedghi, “Color Image Retrieval Technique Based On EM Segmentation Algorithm,” IEEE 5th International Symposium on Telecommunications (IST'2010), pp.793-795, 2010.

Zhenhua Zhang, Wenhui Li, Bo Li, “An Improving technique of color Histogram in segmentation-based Image Retrieval,” IEEE International conference on Information Assurance and Security, vol. 2, pp.381 – 384, 2009.

Ching-Hung Su, Huang-Sen Chiu and T Sai-Ming Hseih, “An efficient image retrieval based on HSV color space,” IEEE International Conference Electrical and Control Engineering (ICECE), pp. 5746 – 5749, 2011.

Noureddine Abbadeni,“Computational Perceptual Features for Texture Representation and Retrieval,” IEEE Trans. Image Process, vol. 20, No.1, Jan 2011.

V.Balamurugan,P.Anandhakumar,“Multiresolution Image Indexing Technique Based on Texture Features Using 2D Wavelet Transform,” European Journal of Scientific Research, ISSN 1450-216X Vol.48 No.4 (2011), pp.648-644, 2011.

Alarmel Mangai, M., Ammasai Gounden, N., Subspace-based learning for face retrieval, (2012) International Review on Computers and Software (IRECOS), 7 (1), pp. 122-131.

S. Liapis and G. Tziritas, “Color and texture image retrieval using chromaticity histograms and wavelet frames,” IEEE Trans. Multimedia, vol. 6, no. 5, pp. 676–686, Oct. 2004.

Ja-Hwung Su, Wei-Jyun Huang, Philip S. Yu, Fellow, IEEE, and Vincent S. Tseng, Member, IEEE, “Efficient Relevance Feedback for Content-Based Image Retrieval by Mining User Navigation Patterns,” IEEE Trans. Knowl. Data Eng., vol. 23, no. 3, Mar 2011.

S. C. Hoi, M. R. Lyu, and R. Jin, “A unified log-based relevance feedback scheme for image retrieval,” IEEE Trans. Knowl. Data Eng., vol. 18, no. 4, pp. 509–524, Apr. 2006.

En Cheng, Feng Jing, and Lei Zhang, “A Unified Relevance Feedback Framework for Web Image Retrieval,” IEEE Trans. Image Process, vol. 18, no. 6, June 2009.

M. R. Azimi-Sadjadi, Senior Member, IEEE, Jaime Salazar, and Saravanakumar Srinivasan, “An Adaptable Image Retrieval System With Relevance Feedback Using Kernel Machines and Selective Sampling,” IEEE Trans. Image Process, vol. 18, no. 7, July 2009.

C. T. Hsu, Member, IEEE, and C. Y. Li, “Relevance Feedback Using Generalized Bayesian Framework With Region-Based Optimization Learning,” IEEE Trans. Image Process, vol. 14, no. 10, pp. 1617- 1631, Oct 2005.

C. Y. Li and C. T. Hsu, “Image Retrieval With Relevance Feedback Based On Graph-theoretic Region correspondence Estimation,” IEEE Trans. Multimedia, vol. 10, no.3, pp. 447-456, Apr 2008.

Dewen Zhuang and Shoujue Wang , “Content-based image retrieval based on integrating region segmentation and relevance feedback,” in Proc. IEEE International Conference on Multimedia Technology (ICMT), pp. 1 - 3 , 2010.

S. Huang, C. K. Dagli, Shyamsundar Rajaram, E. Y. Chang, M. I. Mandel, G. E. Poliner, and D. P. W. Ellis, “Active learning for interactive multimedia retrieval,” in Proceedings of IEEE, vol. 96, no. 4, pp. 648–667, Apr. 2008.

Yudong Zhang and Lenan Wu, “A Novel Method for Rigid Image Registration based on Firefly Algorithm,” International Journal of Research and Reviews in Soft and Intelligent Computing (IJRRSIC), Vol. 2, No. 2, June 2012.

Yudong Zhang and Lenan Wu, "Rigid Image Registration by PSOSQP Algorithm," Advances in Digital Multimedia, vol. 1, pp. 4- 8, 2012.

Herbert M. Gomes,” A firefly metaheuristic algorithm for structural Size and shape optimization with dynamic constraints” Asociación Argentina de Mecánica Computacional, pp. 2059-207 "4, 2011.

Olympia Roeva, “Optimization of E. coli Cultivation Model Parameters Using Firefly Algorithm” Int. J. Bioautomation, vol.16, pp. 23-32, 2012.

Philipp Krahenbuhl and Vladlen Koltun,”Efficient inference in fully connected crfs with gaussian edge potentials”,In NIPS, 2011

Biolngenium Research Group, Universidad Nacional De Colombia. (2012) [Online]. Available: http://www.informed.unal.edu.co:8084/ BiMed/.

H. Akakin and M. Gurcan, “Content-based microscopic image retrieval system for multi-image queries,” IEEE Trans. Inf. Technol. Biomed., vol. 16, no. 4, pp. 758–769, Jul. 2012.


Refbacks

  • There are currently no refbacks.



Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2022 Praise Worthy Prize