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”.