Cuckoo Search Framework For Feature Selection And Classifier Optimization In Compressed Medical Image Retrieval

Reddi Kiran Kumar*, Vamsidhar Enireddy**
*Assistant Professor, Department of Computer Science and Engineering, Krishna University, Machilipatnam, Andhra Pradesh, India
**Research Scholar, Department of Computer Science and Engineering, Jawaharlal Nehru Technological University, Kakinada, India.
Periodicity:January - March'2016

Abstract

With the availability of different medical imaging equipment for diagnoses, medical professionals are increasingly depending on the computer aided techniques for retrieving similar images from large repositories. This work investigates medical image retrieval problem for lossless compressed images. Lossless compression technique is utilized for compressing the medical images for easy transmission and storage. Texture features are extracted using Gabor filters, Shape features using the Gabor - shape and best features of these are selected by using a novel Cuckoo Search algorithm and compared with other statistical techniques. Classification was done by using the Recurrent neural Network. Optimization of the neural network is done using the Cuckoo Search. Experimental results show the advantages of the proposed framework.

Keywords

Keywords: Image Compression, Image Retrieval, Daubechies Wavelet, Gabor Filter, Feature Selection, Cuckoo Search, Recurrent Neural Network.

How to Cite this Article?

Reddi, K. K., and Enireddy, V. (2016). Cuckoo Search Framework For Feature Selection And Classifier Optimization In Compressed Medical Image Retrieval. i-manager's Journal on Image Processing, 3(1), 1-12.

References

[1]. Ueno I., & Pearlman W. A, (2003). “Region-of-interest coding in volumetric images with shape-adaptive wavelet transform”. In Electronic Imaging 2003 International Society for Optics and Photonics, pp. 1048- 1055.
[2]. Dezhgosha, K., Sylla, A. K., & Ngouyassa, E. (1994). “Lossless and lossy image compression algorithms for onboard processing in spacecrafts”. In Aerospace and Electronics Conference, 1994, NAECON 1994, Proceedings of the IEEE 1994 National, pp. 416-423.
[3]. Srikanth, R., & Ramakrishnan A. G, (2005). “Contextual encoding in uniform and adaptive meshbased lossless compression of MR images”. Medical Imaging, IEEE Transactions on, Vol. 24, No.9, pp. 1199- 1206.
[4]. ME, S. S., Vijayakuymar, V. R., & Anuja, R. (2012). “A Survey on Various Compression Methods for Medical Images”. International Journal of Intelligent Systems and Applications (IJISA), Vol.4, No. 3, pp. 13.
[5]. Xiong, Z., Wu, X., Cheng, S., & Hua, J. (2003). “Lossyto- lossless compression of medical volumetric data using three-dimensional integer wavelet transforms”. Medical Imaging, IEEE Transactions, Vol. 22, No. 3, pp. 459-470.
[6]. Zukoski, M. J., Boult, T., & Iyriboz T, (2006). “A novel approach to medical image compression”. International Journal of Bioinformatics Research and Applications, Vol. 2, No. 1, pp. 89-103.
[7]. Yang, L., Jin, R., Mummert, L., Sukthankar, R., Goode, A., Zheng, B., & Satyanarayanan M, (2010). “A boosting framework for visuality-preserving distance metric learning and its application to medical image retrieval”. Pattern Analysis and Machine Intelligence, IEEE Transactions, Vol. 32, No. 1, pp. 30-44.
[8]. Syam, B., Victor, J. S. R., & Rao, Y. S. (2013). “Efficient similarity measure via Genetic algorithm for content based medical image retrieval with extensive features”. In Automation, Computing, Communication, Control and Compressed Sensing (iMac4s), 2013 International Multi- Conference on, IEEE, pp. 704-711.
[9]. Han, J. H., Huang, D. S., Lok, T. M., & Lyu, M. R. (2005). “A novel image retrieval system based on BP neural network ”. In Neural Networks, 2005, IJCNN'05, Proceedings, 2005 IEEE International Joint Conference on, Vol. 4, pp. 2561-2564.
[10]. Bugatti, P. H., Ribeiro, M. X., Traina, A. J. M., & Traina, C. (2008). “Content-based retrieval of medical images by continuous feature selection”. In Computer-Based Medical Systems, 2008. CBMS'08, 21st IEEE International Symposium on, pp. 272-277.
[11]. Muneesawang, P., & Guan, L. (2002). “Automatic machine interactions for content-based image retrieval using a self-organizing tree map architecture”. Neural Networks, IEEE Transactions on, Vol. 13, No. 4, pp. 821- 834.
[12]. Kumar, M. S., & Kumaraswamy, Y. S. (2012). “An improved support vector machine kernel for medical image retrieval system”. In Pattern Recognition, Informatics and Medical Engineering (PRIME), 2012 International Conference on, pp. 257-260.
[13]. Hussain, S. J., Savithri, A. S., & Devi, P. V. S. (2011). “Segmentation of brain MRI with statistical and 2D wavelet features by using neural networks”. In Trendz in Information Sciences and Computing (TISC), 2011 3rd International Conference on, pp. 154-159. IEEE.
[14]. Daubechies, I., & Bates, B. J. (1993). “Ten lectures on wavelets”. The Journal of the Acoustical Society of America, Vol. 93, No. 3, pp. 1671-1671.
[15]. Abdullah, M. S., & Rao, N. S. (2013). “Image Compression using Classical and Lifting based Wavelets”. Image, Vol. 2, No.8.
[16]. Aggarwal, M., & Narayan, A. (2000). “Efficient huffman decoding”. In Image Processing, 2000, Proceedings, 2000 International Conference on, Vol. 1, pp. 936-939.
[17]. Pujar, J. H., & Kadlaskar, L. M. (2010). “A New lossless method of image compression and decompression using huffman coding techniques”. Journal of Theoretical & Applied Information Technology, Vol. 15.
[18]. Kekre, H. B., & Bharadi, V. A. (2010). “Gabor filter based feature vector for dynamic signature recognition”. International Journal of Computer Applications, Vol. 2, No.3, pp. 74-80.
[19]. Jemaa, Y. B., & Khanfir, S. (2009). “Automatic local Gabor features extraction for face recognition”. arXiv preprint arXiv:0907.4984.
[20]. Choras, R. S. (2007). “Image feature extraction techniques and their applications for CBIR and biometrics systems”. International Journal of Biology and Biomedical Engineering, Vol. 1, No. 1, pp. 6-16.
[21]. ZRamaswami, M., & Bhaskaran, R. (2009). “A study on feature selection techniques in educational data mining”. arXiv preprint arXiv:0912.3924.
[22]. Tourassi, G. D., Frederick, E. D., Markey, M. K., & Floyd Jr, C. E. (2001). “Application of the mutual information criterion for feature selection in computeraided diagnosis”. Medical Physics, Vol. 28, No.12, pp.2394-2402
[23]. Tiwari, V. (2012). “Face Recognition Based on Cuckoo Search Algorithm”. IJCSE, image, Vol. 7, No. 8, pp. 9.
[24]. Yang, X. S. and Deb, S., (2009). “Cuckoo search via Lévyflights”. In: Proc. of World Congress on Nature & Biologically Inspired Computing (NaBIC 2009), pp. 210- 214.
[25]. Yang, X.S., and Deb, S. (2010). “Engineering Optimisation by Cuckoo Search”. Int. J. of Mathematical Modelling and Numerical Optimisation, Vol. 1, No. 4, pp. 330– 343.
[26]. Tuba, M., Subotic, M., & Stanarevic, N. (2011). “Modified cuckoo search algorithm for unconstrained th optimization problems”. In Proceedings of the 5 European Conference on European Computing Conference, pp. 263-268.
[27]. Yang, X. S., & Deb, S. (2010). “Engineering optimisation by cuckoo search”. International Journal of Mathematical Modelling and Numerical Optimisation, Vol. 1, No. 4, pp. 330-343.
[28] X.-S. Yang; S. Deb (2009). “Cuckoo search via Lévy flights”. World Congress on Nature & Biologically Inspired Computing (NaBIC 2009), IEEE Publications. pp. 210–214.
[29]. Pal, S. K., & Mitra, S. (1992). “Multilayer perception, fuzzy sets, and classification”. IEEE Transactions on Neural Networks, Vol. 3, No. 5, pp. 683-697.
[30]. Fausett, L. (1994). Fundamentals of Neural Networks; Architectures, Algorithms and Applications. Prentice-Hall, Inc. New Jersey, 07632.
[31]. Hecht - Nielsen R, (1989). “Theory of the back propagation neural network”. In Neural Networks, 1989. IJCNN, International Joint Conference on, pp. 593-605.
[32]. Elman J. L, (1990). “Finding Structure in Time”. Cognitive Science, Vol. 14, pp. 179–211.
[33]. Jordan M, (1986). “Attractor Dynamics and parallelism in a connectionist sequence machine”. In: Proc of Ninth Annual conference of Cognitive Science Society. Lawrence Earlbaum, New York, pp. 531–546.
[34]. Martens, J., & Sutskever I, (2011). “Learning Recurrent Neural Networks with Hessian-Free Optimization”. In Proceedings of the 28th International Conference on Machine Learning (ICML-11), pp. 1033- 1040.
[35]. Bodén M, (2002). “A guide to recurrent neural networks and back propagation”. The Dallas Project, SICS Technical Report.
[36]. I. J. Koscak., (2010). “Stochastic Weight Selection in Back propagation Through Time”.
If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Single Article

North Americas,UK,
Middle East,Europe
India Rest of world
USD EUR INR USD-ROW
Pdf 35 35 200 20
Online 35 35 200 15
Pdf & Online 35 35 400 25

Options for accessing this content:
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.