Abdulbaki, A. S. (2012). Skin cancer image segmentation & detection by using unsupervised

machine learning.

 

Aher, M. S. V., & Vasekar, M. S. (2016). A Review: Histogram Equalization Algorithms for Image  

Enhancement  using  FPGA.  International  Journal  of  Advanced  Research  in Computer and 

Communication Engineering, 5(4), 711-714.

 

Ahmad, M., Gupta, C., & Varshney, A. (2009, March). Digital image encryption based on chaotic   map 

  for   secure   transmission.   In   Multimedia,   Signal   Processing   and Communication 

Technologies, 2009. IMPACT'09. International (pp. 292-295). IEEE.

 

Ahmed, I. N., & Chaya, P. (2014). Segmentation and Classification of Skin Cancer Images.

International Journal, 4(5).

 

Al-Dmour, H., & Al-Ani, A. (2016). Quality optimized medical image information hiding algorithm 

that employs edge detection and data coding. Computer methods and programs in biomedicine, 127, 

24-43.

 

Al-Qershi, O. M., & Khoo, B. E. (2011). High capacity data hiding schemes for medical images based 

on difference expansion. Journal of Systems and Software, 84(1), 105-112.

 

Alwan, I. M., & Mohammed, F. J. (2016). Image Hiding Using Discrete Cosine Transform.

Journal Of The College Of Education For Women, 27(1), 393-399.

 

An, L., Gao, X., Yuan, Y., Tao, D., Deng, C., & Ji, F. (2012). Content-adaptive reliable robust 

lossless data embedding. Neurocomputing, 79, 1-11.

 

Anees, A., Siddiqui, A. M., Ahmed, J., & Hussain, I. (2014). A technique for digital steganography 

using chaotic maps. Nonlinear Dynamics, 75(4), 807-816.

 

Ansari, S., Gupta, N., & Agrawal, S. (2012). A Review on Chaotic Map Based Cryptography.

International Journal of Scientific Engineering and Technology.

 

Anthony, G., Greg, H., & Tshilidzi, M. (2007). Classification of images using support vector 

machines. arXiv preprint arXiv:0709.3967.

 

Arsalan, M., Malik, S. A., & Khan, A. (2012). Intelligent reversible watermarking in integer

wavelet domain for medical images. Journal of Systems and Software, 85(4), 883-894.

 

Barani, M. J., Valandar, M. Y., & Ayubi, P. (2015, May). A secure watermark embedding

approach  based  on  chaotic  map  for  image  tamper  detection.  In Information  and Knowledge 

Technology (IKT), 2015 7th Conference on (pp. 1-5). IEEE.

 

Bard, R. L. (2017). High-Frequency Ultrasound Examination in the Diagnosis of Skin Cancer.

Dermatologic Clinics.

 

Bhuiyan, M. A. H., Azad, I., & Uddin, K. (2013). Image processing for skin cancer features 

extraction. International Journal of Scientific & Engineering Research, 4(2), 1-6.

 

Bilal, M., Imtiaz, S., Abdul, W., Ghouzali, S., & Asif, S. (2014). Chaos based Zero- steganography 

algorithm. Multimedia tools and applications, 72(2), 1073-1092.

 

Bloch, I. (2015). Fuzzy sets for image processing and understanding. Fuzzy Sets and Systems, 281, 

280-291.

 

Botta, M., Cavagnino, D., & Pomponiu, V. (2015). A successful attack and revision of a chaotic 

system based fragile watermarking scheme for image tamper detection. AEU- International Journal of 

Electronics and Communications, 69(1), 242-245.

 

Bremnavas, I., Poorna, B., & Kanagachidambaresan, G. R. (2011). Medical image security using LSB 

and chaotic logistic map.

 

Caragata, D., El Assad, S., & Luduena, M. (2015). An improved fragile watermarking algorithm for 

JPEG images. AEU-International Journal of Electronics and Communications, 69(12), 1783-1794.

 

Celebi, M. E., Iyatomi, H., Schaefer, G., & Stoecker, W. V. (2009). Lesion border detection in 

dermoscopy images. Computerized medical imaging and graphics, 33(2), 148-153.

 

Celik, M. U., Sharma, G., & Tekalp, A. M. (2006). Lossless watermarking for image authentication: a 

new framework and an implementation. IEEE Transactions on Image Processing,15(4), 1042-1049.

 

Chadha, A., Mallik, S., & Johar, R. (2012). Comparative study and optimization of feature- 

extraction techniques for content based image retrieval. arXiv preprint arXiv:1208.6335.

 

Chakravorty, R., Liang, S., Abedini, M., & Garnavi, R. (2016, August). Dermatologist-

like feature extraction from skin lesion for improved asymmetry classification in PH 2 database. In 

Engineering in Medicine and Biology Society (EMBC), 2016 IEEE 38th

Annual International Conference of the (pp. 3855-3858). IEEE.

 

Chandra, G., Chandra, N., & Verma, S. (2015). A Review on Multiple Chaotic Maps for Image

Encryption    with    Cryptographic    Technique. International    Journal    of    Computer

Applications, 121(13).

 

Chang, C. C., & Tseng, H. W. (2009, June). Data hiding in images by hybrid LSB substitution.

In Multimedia and Ubiquitous Engineering, 2009. MUE'09. Third International Conference.

 

Chang, C. C., Chen, K. N., Lee, C. F., & Liu, L. J. (2011). A secure fragile watermarking scheme 

based on chaos-and-hamming code. Journal of Systems and Software, 84(9), 1462-1470.

 

Chettri, R., Pradhan, S., & Chettri, L. (2015). Internet of Things: Comparative Study on 

Classification Algorithms (k-NN, Naive Bayes and Case based Reasoning). International Journal of 

Computer Applications, 130(12), 7-9.

 

Chitaliya, N. G., & Trivedi, A. I. (2010, March). Feature extraction using wavelet-pca and neural 

network for application of object classification & face recognition. In Computer Engineering and 

Applications (ICCEA), 2010 Second International Conference on (Vol. 1, pp. 510-514). IEEE.

 

Chitla, A., & Chandra Mohan, M. (2014). Authenticating Medical Images With Lossless Digital 

Watermarking. International Journal Of Multidisciplinary And Current Research, 2.

 

Chitla, A., & Chandra Mohan, M. (2014). Authenticating Medical Images With Lossless Digital 

Watermarking. International Journal Of Multidisciplinary And Current Research, 2.

 

Çiftçi, G. (2003). Shape Analysis Using Contour-Based and Region-Based Approaches. Doctoral 

dissertation, Middle East Technical University.

 

Cui, S., Jiang, H., Wang, Z., & Shen, C. (2017, June). Application of neural network based on SIFT 

local feature extraction in medical image classification. In Image, Vision and Computing (ICIVC), 

2017 2nd International Conference on (pp. 92-97). IEEE.

 

Dalila, F., Zohra, A., Reda, K., & Hocine, C. (2017). Segmentation and Classification of Melanoma 

and Benign Skin Lesions. Optik-International Journal for Light and Electron Optics.

 

 

Das, S., & Kundu, M. K. (2013). Effective management of medical information through ROI- lossless 

fragile image watermarking technique. Computer methods and programs in

biomedicine,111(3), 662-675.

 

 

Demidova, L., Nikulchev, E., & Sokolova, Y. (2016). Big data classification using the SVM

classifiers with the modified particle swarm optimization and the SVM ensembles. International 

Journal of Advanced Computer Science and Applications (IJACSA), 7(5), 294-312.

 

Denil, M., Matheson, D., & De Freitas, N. (2013). Narrowing the gap: Random forests in theory and 

in practice. Cornell University Library:arXiv preprint arXiv:1310.1415.

 

Diaconu, A. V. (2016). Circular inter–intra pixels bit-level permutation and chaos-based image 

encryption. Information Sciences, 355, 314-327.

 

Dimitrovski, I., Kocev, D., Kitanovski, I., Loskovska, S., & D?eroski, S. (2015). Improved medical 

image modality classification using a combination of visual and textual features. Computerized 

Medical Imaging and Graphics, 39, 14-26.

 

Dobbin, K. K., & Simon, R. M. (2011). Optimally splitting cases for training and testing high 

dimensional classifiers. BMC medical genomics, 4(1), 1.

 

 

Drew, T., Evans, K., Võ, M. L. H., Jacobson, F. L., & Wolfe, J. M. (2013). Informatics in 

radiology: what can you see in a single glance and how might this guide visual search in medical 

images?. Radiographics, 33(1), 263-274.

 

Elgamal, M. (2013). Automatic skin cancer images classification. IJACSA) International Journal of 

Advanced Computer Science and Applications, 4(3), 287-294.

 

 

Fakhari, P., Vahedi, E., & Lucas, C. (2011). Protecting patient privacy from unauthorized release 

of medical images using a bio-inspired wavelet-based watermarking approach. Digital Signal 

Processing, 21(3), 433-446.

 

Fawcett, T., (2003). ROC graphs: notes and practical consideration for researchers, Technical 

Report, HP Lab, 1-27.

 

Garbe, C., Peris, K., Hauschild, A., Saiag, P., Middleton, M., Spatz, A., ... & Pehamberger, H. 

(2012). Diagnosis and treatment of melanoma. European consensus-based interdisciplinary 

guideline–Update 2012. European journal of cancer, 48(15), 2375-

2390.

 

Garcia-Hernandez, J. J., Gomez-Flores, W., & Rubio-Loyola, J. (2016). Analysis of the impact of 

digital watermarking on computer-aided diagnosis in medical imaging. Computers in biology and 

medicine, 68, 37-48.

 

Gasparini, F., Corchs, S., and Schettini, R., (2008). Recall or precision-oriented strategies for

binary classification of skin pixels. Journal of Electronic Imaging, 17(2), 1-15.

 

 

Ghebleh, M., & Kanso, A. (2014). A robust chaotic algorithm for digital image steganography.

Communications in Nonlinear Science and Numerical Simulation, 19(6), 1898-1907.

 

Goel, R., Kumar, V., Srivastava, S., Sinha, A. K. (2017). A Review of Feature Extraction Techniques 

for Image Analysis. International Conference on Advances in Computational Techniques and Research 

Practices, Vol. 6(Special Issue 2).

 

Gonzalez, R.C., Woods, R.E., and Eddins, S.L., (2009). Digital image processing using MATLAB. 

Gatesmark Publishing, Second Edition, 827 pages

Gu, Q., & Gao, T. (2013). A novel reversible robust watermarking algorithm based on chaotic system. 

Digital Signal Processing, 23(1), 213-217.

 

Guzella, T.S., and Caminhas, W.M., (2009). A review of machine learning approaches to spam

,filtering. Expert System with Application, 36(7), 10206-10222.

 

Hamouda, K., Elmogy, M., & El-Desouky, B. S. (2014, December). A fragile watermarking 

authentication schema based on Chaotic maps and fuzzy cmeans clustering technique. In Computer 

Engineering & Systems (ICCES), 2014 9th International Conference on (pp. 245-252). IEEE.

 

 

Hassan, M. M. (2013). Current studies on intrusion detection system, genetic algorithm and fuzzy 

logic. Cornell University Library:arXiv preprint arXiv: 1304.3535.

 

Hasso, M. A. R., & Elyas, R. M. (2014). Fast Image Registration Based on Features Extraction and 

Accurate Matching Points for Image Stitching. International Journal of Computer Science Issues 

(IJCSI), 11(5), 138.

 

Hemamalini, N., & Aggarwal, L. M. (2010). Automated medical image segmentation techniques.

Journal of medical physics/Association of Medical Physicists of India, 35(1), 3.

 

Hoshyar, A. N., Al-Jumaily, A., & Hoshyar, A. N. (2014). The Beneficial Techniques in Preprocessing 

Step of Skin Cancer Detection System Comparing. Procedia Computer Science,

 

Hota, H. S., Shukla, S. P., & Gulhare, K. (2013). Review of intelligent techniques applied for 

classification  and  preprocessing  of  medical  image  data.  International  Journal  of Computer 

Science Issues, 10(1/3), 267-272.

 

Ibrahim, A. S., Sartep, H. J. (2017). Grayscale Image Coloring by Using YCbCr and HSV Color Spaces. 

IJMTER, VoL. 4(4).

 

Iftikhar, S., Kamran, M., & Anwar, Z. (2015). A survey on reversible watermarking techniques for 

relational databases. Security and Communication Networks, 8(15), 2580-2603.

 

Ingale, S. P., & Dhote, C. A. (2016). Digital Watermarking Algorithm using DWT Technique.

IJCSMC, Vol. 5(5), 01 – 0

 

 

Jain, S. (2013). Brain cancer classification using GLCM based feature extraction in artificial

neural network. International Journal of Computer Science & Engineering Technology, 4(7), 966-970.

 

Jain, S., jagtap,, V., Pise, N. (2015). Computer aided melanoma skin cancer detection using image   

 processing. Procedia Computer Science, 48, 735-740.

 

Jasani, D., Patel, P., Patel, S., Ahir, B., Patel, K., & Dixit, M. (2015). Review of shape and 

texture feature extraction techniques for fruits. International Journal of Computer Science and 

Information Technologies, 6(6), 4851-4854.

 

Jaskaran Singh, & Anoop Kumar Patel, (2017). Region of Interest Based Adaptable Watermarking 

Technique for Medical Images . International Journal of Advances in Electronics and Computer 

Science ISSN: 2393-2835 Volume-4, Issue-11.

 

Jianli, L., & Baoqi, Z. (2009, March). The segmentation of skin cancer image based on genetic 

neural network. In Computer Science and Information Engineering, 2009 WRI World Congress on (Vol. 

5, pp. 594-599). IEEE.

 

Joshi, S., Pandey, B., & Joshi, N. (2015). Comparative analysis of Naive Bayes and J48 

classification.  International Journal of Advanced Research in  Computer Science  and Software 

Engineering, 5(12), 813-7.

 

Kadhim, Q. K. (2016). Image Compression Using Discrete Cosine Transform Method.

International Journal of Computer Science and Mobile Computing,Vol.5 (9).

 

Kakumanu, P., Makrogiannis, S., & Bourbakis, N. (2007). A survey of skin-color modeling and 

detection methods. Pattern recognition, 40(3), 1106-1122.

 

Kanan, C., & Cottrell, G. W. (2012). Color-to-grayscale: does the method matter in image 

recognition?. PloS one, 7(1), e29740.

 

Kanso, A., & Own, H. S. (2012). Steganographic algorithm based on a chaotic map.

Communications in Nonlinear Science and Numerical Simulation, 17(8), 3287-3302.

 

Kapoor, K., & Arora, S. (2015). Colour image enhancement based on histogram equalization.

Electrical & Computer Engineering: An International Journal, 4(3), 73-82.

 

Kaur, D., & Kaur, Y. (2014). Various Image Segmentation Techniques: A Review. International Journal 

of Computer Science and Mobile Computing, 3(5), 809-814.

 

Keshari, S., & Modani, S. G. (2011). Image Encryption Algorithm based on Chaotic Map Lattice

and Arnold cat map for Secure Transmission 1.

 

Khalind, O., & Aziz, B. (2013, December). Single-mismatch 2LSB embedding steganography.

In Signal Processing and Information Technology (ISSPIT), 2013 IEEE International Symposium on (pp. 

000283-000286). IEEE.

 

Khan, W. (2013). Image segmentation techniques: A survey. Journal of Image and Graphics, 1(4), 

166-170.

 

Khor, H. L., Liew, S. C., & Zain, J. M. (2017). Region of Interest-Based Tamper Detection and 

Lossless Recovery Watermarking Scheme (ROI-DR) on Ultrasound Medical Images. Journal of digital 

imaging, 30(3), 328-349.

 

Kone, C., Le Thanh, N., Flamary, R., & Belleudy, C. (2018). Performance Comparison of the KNN and 

SVM Classification Algorithms in the Emotion Detection System EMOTICA. International Journal of 

Sensor Networks and Data Communications.

 

Koppu, S., & Viswanatham, V. M. (2017). A Fast Enhanced Secure Image Chaoti Cryptosystem Based on 

Hybrid Chaotic Magic Transform. Modelling and Simulation in Engineering, 2017.

 

Krishna, M. C., Ranganayakulu, S., Venkatesan, P. (2016). Skin Cancer Detection and Feature 

Extraction through Clustering Technique. IJIRCCE, Vol. 4(3).

 

Kumar, M. ( 2003). DIGITAL IMAGE PROCESSING. Satellite Remote Sensing and GIS Applications in 

Agricultural Meteorology, India.

 

Kumravat, S. (2013). An efficient steganographic scheme using skin tone detection and discrete 

wavelet transformation. Int J Comput Sci Eng Technol (IJCSET), 4(7), 971-976.

 

Lau, H. T., & Al-Jumaily, A. (2009, December). Automatically early detection of skin cancer: Study 

based on nueral netwok classification. In Soft Computing and Pattern Recognition, 2009. SOCPAR'09. 

International Conference of (pp. 375-380). IEEE.

 

Lazarov, N., & Ilcheva, Z. (2016, September). A fragile watermarking algorithm for image tamper 

detection based on chaotic maps. In Intelligent Systems (IS), 2016 IEEE 8th International 

Conference on (pp. 723-728). IEEE.

 

Lee, H., & Chen, Y. P. P. (2015). Image based computer aided diagnosis system for cancer detection. 

Expert Systems with Applications, 42(12), 5356-5365.

 

Lee, H., & Chen, Y. P. P. (2015). Image based computer aided diagnosis system for cancer detection. 

Expert Systems with Applications, 42(12), 5356-5365.

 

Lee, J., Pant, S. R., & Lee, H. S. (2015). An adaptive histogram equalization based local technique 

for contrast preserving image enhancement. International Journal of Fuzzy

Logic and Intelligent Systems, 15(1), 35-44.

 

Lei, B., & Soon, Y. (2015). Perception-based audio watermarking scheme in the compressed

bitstream. AEU-International Journal of Electronics and Communications, 69(1), 188- 197.

 

Li, S. Q., & Wu, Y. (2010, April). A Robust Chaos-Based Watermarking for Copyright Protection.   In 

Biomedical   Engineering   and   Computer   Science   (ICBECS),   2010 International Conference on 

(pp. 1-3). IEEE.

 

Liao, X., & Wen, Q. Y. (2008, December). Embedding in two least significant bits with wet paper  

coding.  In  2008  International  Conference  on  Computer  Science  and  Software

Engineering (pp.555-558). IEEE.

 

Liu, L., & Miao, S. (2016). A new image encryption algorithm based on logistic chaotic map with 

varying parameter. SpringerPlus, 5(1), 289.

 

López-Hernández, J., Vazquez-Medina, R., Ortiz-Moctezuma, M. B., & Del-Rio-Correa, J. L. (2012). 

Digital implementation of a pseudo-random noise generator using chaotic maps. IFAC Proceedings 

Volumes, 45(12), 209-214.

 

Lorentzon, M. (2017). Feature extraction for image selection using machine learning. Master thesis, 

Linköping University, Sweden.

 

Loussaief, S., & Abdelkrim, A. (2017, July). Machine learning framework for image classification.  

In  Information  and  Digital  Technologies  (IDT),  2017  International Conference on (pp. 58-61). 

IEEE.

 

Ma, Z., & Tavares, J. M. R. (2015). A review of the quantification and classification of pigmented 

skin lesions: From dedicated to hand-held devices. Journal of medical systems, 39(11), 177.

 

Mahmoud, M. K. A., & Al-Jumaily, A. (2011, August). Segmentation of skin cancer images based on 

gradient vector flow (GVF) snake. In Mechatronics and Automation (ICMA), 2011 International 

Conference on (pp. 216-220). IEEE.

 

Mandal, A. K., & Baruah, D. K. (2013). Image Segmentation Using Local Thresholding And Ycbcr Color 

Space. Journal of Engineering Research and Applications, Vol. 3 (6), pp. 511-514.

 

Mandloi, G. (2014). A survey on feature extraction techniques for color images. International

Journal of Computer Science and Information Technologies, 5(3), 4615-4620. 

 

Manojbhai, D. D., & Rajamenakshi, R. (2016). Large Scale Image feature extraction from

medical image analysis. International journal of advanced engineering and research.

 

Masood, A., & Ali Al-Jumaily, A. (2013). Computer aided diagnostic support system for skin

cancer: a review of techniques and algorithms. International journal of biomedical imaging, 2013.

 

Medhi, S., Ahmed, C., & Gayan, R. (2016). A Study on Feature Extraction Techniques in Image 

Processing.  International Journal of Computer Sciences and Engineering,  Vol.4 (Special Issue-7).

 

Mendonca Chahar, P. S., & Thakare, V. (2015). Performance Comparison of Various Filters for 

Removing Gaussian and Poisson Noises. International Research Journal of Engineering and Technology, 

2(5), 1101-1105.

 

Mhaske, H. R., & Phalke, D. A. (2013, December). Melanoma skin cancer detection and classification 

based on supervised and unsupervised learning. In Circuits, Controls and Communications (CCUBE), 

2013 International conference on (pp. 1-5). IEEE.

 

Mishra, A., Rai, A., & Yadav, A. (2014). Medical image processing: A challenging analysis.

International Journal of Bio-Science and Bio-Technology, 6(2), 187-194.

 

Mitra, S., & Shankar, B. U. (2015). Medical image analysis for cancer management in natural 

computing framework. Information Sciences, 306, 111-131.

 

Mohammadi, S. (2015, September). A chaotic watermarking scheme using discrete cosine transform. In 

Information Security and Cryptology (ISCISC), 2015 12th International Iranian Society of Cryptology 

Conference on (pp. 6-10). IEEE.

 

Moniruzzaman, M., Hawlader, M. A. K., & Hossain, M. F. (2014, May). An image fragile watermarking  

scheme  based  on  chaotic  system  for  image  tamper  detection.  In Informatics, Electronics & 

Vision (ICIEV), 2014 International Conference on (pp. 1-6). IEEE.

 

Moss, H. B., Leslie, D. S., & Rayson, P. (2018). Using JK fold Cross Validation to Reduce Variance 

When Tuning NLP Models. arXiv preprint arXiv:1806.07139.

 

Murumkar, O. S., & Gumaste, P. P. (2015). Feature Extraction for Skin Cancer Lesion  Detection.  

International  Journal  of  Science,  Engineering  and  Technology  Research (IJSETR), 4(5).

 

Naheed, T., Usman, I., Khan, T. M., Dar, A. H., & Shafique, M. F. (2014). Intelligent reversible 

watermarking  technique  in  medical  images  using  GA  and  PSO.  Optik-International Journal for 

Light and Electron Optics, 125(11), 2515-2525.

 

Nambakhsh, M. S., Ahmadian, A., & Zaidi, H. (2011). A contextual based double watermarking of PET 

images  by patient  ID and ECG signal. Computer methods and programs in

biomedicine, 104(3), 418-425.

 

Nammalwar, P., Ghita, O., & Whelan, P. F. (2009). Segmentation of skin cancer images. Google

scholar.

 

Naseem, M. T., Qureshi, I. M., & Muzaffar, M. Z. (2013, December). Chaos based invertible

authentication of medical images. In Emerging Technologies (ICET), 2013 IEEE 9th

International Conference on (pp. 1-5). IEEE.

 

Natanj, S., & Taghizadeh, S. R. (2011). Current Steganography Approaches: A survey.

International Journal of Advanced Research in Computer Science and Software

Engineering, 1.

 

Naveed, A., Saleem, Y., Ahmed, N., & Rafiq, A. (2015). Performance evaluation and watermark

security assessment of digital watermarking techniques. Science International, 27(2).

 

Oliveira, R. B., Marranghello, N., Pereira, A. S., & Tavares, J. M. R. (2016). A computational

approach for detecting pigmented skin lesions in macroscopic images. Expert Systems

with Applications, 61, 53-63.

 

Orozco, J., & García, C. A. R. (2003, April). Detecting pathologies from infant cry applying

scaled conjugate gradient neural networks. In European Symposium on Artificial Neural

Networks, Bruges (Belgium) (pp. 349-354).

 

Patel, M. N., & Tandel, P. (2016). A Survey on Feature Extraction Techniques for Shape based

Object Recognition. Image, 137(6).

 

Patil, A. B., & Shaikh, J. A. (2016). Segmentation and Feature Extraction of Flowers Intended

for Image Retrieval: A survey .IJARECE, Vol. 5(1).

 

Pereira, S. Voloshynovskiy, S., Pun, T. (2000). Optimized wavelet domain watermark

embedding strategy using linear programming, in: Proceedings of SPIE on AeroSense,

Orlando, Florida, USA, 2000, pp. 490–498.

 

Pereira, S., & Pun, T. (1999, September). Fast robust template matching for affine resistant

image watermarks. In Information Hiding (pp. 199-210).

 

Preethi, B. C., & Abraham, G. E. (2016). Lung Tissue Extraction Using OTSU Thresholding in

Lung Nodule Detection from CT Images. International Journal of Current Trends in

Engineering & Technology , 2(06).

 

Ram, B., Kumar,M. (2013). Digital image watermarking technique using discrete wavelet

transform and discrete cosine transform. International journal of Advancements in

Research & technology, 2(4), 19-27.

 

Rawat, S., & Raman, B. (2011). A chaotic system based fragile watermarking scheme for image

tamper detection. AEU-International Journal of Electronics and Communications, 65(10),

840-847.

 

Ren, W., Hu, L., Zhao, K., Chu, J., & Jia, B. (2013). Intrusion Classifier based on Multiple

Attribute Selection Algorithms. Journal of Computers, 8(10), 2536-2543.

 

Renu Bala (2016). Image Edge Detection using Discrete Wavelet Transform. International

Journal of Innovative Research in Computer and Communication Engineering, Vol. 4

(Special Issue 4).

 

Review on Different Chaotic Based Image Encryption Techniques. Intern. Journal of Info. and

Computation Tech.Vol. 4(2), pp. 197-206.

 

Roy, K. K., & Phadikar, A. (2014). Automated Medical Image Segmentation: A Survey. In Proc.

of Int. Conf. on Computing, Communication & Manufacturing.

 

Roy, R., Sarkar, A., & Changder, S. (2013). Chaos based edge adaptive image steganography.

Procedia Technology, 10, 138-146.

 

Sajasi, S., & Eftekhari-Moghadam, A. M. (2013, April). A high quality image hiding scheme

based upon Noise Visibility Function and an optimal chaotic based encryption method. In

AI & Robotics and 5th RoboCup Iran Open International Symposium (RIOS),

 

Sathisha, N., Madhusudan, G. N., Bharathesh, S., Babu, K. S., Raja, K. B., & Venugopal, K. R.

(2010, July). Chaos based spatial domain steganography using MSB. In Industrial and

Information Systems (ICIIS), 2010 International Conference on (pp. 177-182). IEEE.

 

Scholl, I., Aach, T., Deserno, T. M., & Kuhlen, T. (2011). Challenges of medical image

processing. Computer science-Research and development, 26(1), 5-13.

 

Shahzad, R. K., & Lavesson, N. (2013). Comparative analysis of voting schemes for ensemblebased

malware detection. Journal of Wireless Mobile Networks, Ubiquitous Computing,

and Dependable Applications, 4(1), 98-117.

 

Sharma, N., & Aggarwal, L. M. (2010). Automated medical image segmentation techniques.

Journal of medical physics/Association of Medical Physicists of India, 35(1), 3.

 

Shaw, G.A., and Burke, H.H.K., (2003). Spectral imaging for remote sensing. Lincoln

Laboratory

 

Shejul, A. A., & Kulkarni, U. L. (2010, February). A DWT based approach for steganography

using biometrics. In Data Storage and Data Engineering (DSDE), 2010 International

Conference on (pp. 39-43). IEEE.

 

Shen, Q., Diao, R., & Su, P. (2012). Feature Selection Ensemble. Turing-100, 10, 289-306.

 

Silveira, M., Nascimento, J. C., Marques, J. S., Marçal, A. R., Mendonça, T., Yamauchi, S., ... &

Rozeira, J. (2009). Comparison of segmentation methods for melanoma diagnosis in

dermoscopy images. IEEE Journal of Selected Topics in Signal Processing, 3(1), 35-45.

 

Solomon, C., & Breckon, T. (2011). Fundamentals of Digital Image Processing: A practical

approach with examples in Matlab. John Wiley & Sons.

 

Starck, J. L., Murtagh, F., Candès, E. J., & Donoho, D. L. (2003). Gray and color image contrast

enhancement by the curvelet transform. IEEE Transactions on image processing, 12(6),

706-717.

 

Subramanian, T., Taqa, A.Y., and Jalab, H.A., (2010). Overview of textual anti-spam filtering

,techniques. International Journal of Physical Science, 5 (12), 1869-1882.

 

Suksut, K., Chanklan, R., Kaoungku, N., Chaiyakhan, K., Kerdprasop, N., & Kerdprasop, K.

(2017). Parameter Optimization for Mammogram Image Classification with Support

Vector Machine. In Proceedings of the International MultiConference of Engineers and

Computer Scientists (Vol. 1).

 

Tabash, F. K., Rafiq, M. Q., & Izharrudin, M. (2013). Image encryption algorithm based on

chaotic map. International Journal of Computer Applications, 64(13).

 

Tataru, R. L., El Assad, S., & Déforges, O. (2012, December). Improved blind DCT

watermarking by using chaotic sequences. In Internet Technology And Secured

Transactions, 2012 International Conference for (pp. 46-50). IEEE.

 

Thabit, R., & Khoo, B. E. (2015). A new robust lossless data hiding scheme and its application to

color medical images. Digital Signal Processing, 38, 77-94.

 

Thanh Noi, P., & Kappas, M. (2017). Comparison of random forest, k-nearest neighbor, and

support vector machine classifiers for land cover classification using Sentinel-2 imagery.

Sensors, 18(1), 18.

 

Thongkor, K., & Amornraksa, T. (2014, December). Digital image watermarking with partial

embedding on blue color component. In APSIPA (pp. 1-4).

 

Thorat, C. G., & Jadhav, B. D. (2010). A blind digital watermark technique for color image

based on integer wavelet transform and SIFT. Procedia Computer Science, 2, 236-241.

 

Tong, X., Liu, Y., Zhang, M., & Chen, Y. (2013). A novel chaos-based fragile watermarking for

image tampering detection and self-recovery. Signal Processing: Image Communication,

28(3), 301-308.

 

Trabelsi, O., Tlig, L., Sayadi, M., & Fnaiech, F. (2015, March). Skin lesion segmentation using

the DS evidence theory based on the FCM using feature parameters. In Systems, Signals

& Devices (SSD), 2015 12th International Multi-Conference on (pp. 1-5). IEEE.

 

Tsai, D. Y., Lee, Y., Sekiya, M., & Ohkubo, M. (2004). Medical image classification using

genetic-algorithm based fuzzy-logic approach. Journal of Electronic Imaging, 13(4),

780-788.

 

Tzotso, A. (2006). A support vector machine approach for object based image analysis.

Proceedings of OBIA.

 

Umbaugh, S.E., (2010). Digital image processing and analysis: human and computer applications

with CVIP tools. CRC Press, Second Edition, 977 pages

 

Wang, G., Zuluaga, M. A., Li, W., Pratt, R., Patel, P. A., Aertsen, M., ... & Vercauteren, T.

(2018). DeepIGeoS: a deep interactive geodesic framework for medical image

segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence.

 

Wang, J., Sun, Y., Xu, H., Chen, K., Kim, H. J., & Joo, S. H. (2010). An improved section-wise

exploiting modification direction method. Signal Processing, 90(11), 2954-2964.

 

Wang, P., Wei, Z., & Cao, C. (2010, June). A Fragile Watermarking Algorithm Based on

Logistic System. In Information and Computing (ICIC), 2010 Third International

Conference on (Vol. 4, pp. 304-307). IEEE.

 

Wisaeng, K. (2013). A Comparison of Different Classification Techniques for Bank Direct

Marketing. International Journal of Soft Computing and Engineering (IJSCE), 3(4), 116-

119.

 

Wu, C. M., & Shih, Y. S. (2013). A simple image tamper detection and recovery based on

fragile watermark with one parity section and two restoration sections. Optics and

Photonics Journal, 3(02), 103.

 

Wu, J. K., Kankanhalli, M. S., Lim, J. H., & Hong, D. (2000). Perspectives on content-based

multimedia systems (Vol. 9). Springer Science & Business Media.

 

Xia, R., Zhao, J., & Liu, Y. (2013, October). A robust feature-based registration method of

multimodal image using phase congruency and coherent point drift. In Eighth

International Symposium on Multispectral ImageProcessing and Pattern Recognition (pp.

891903-891908).

 

Xiao, D., & Jin, J. (2012, November). A reversible two-level image authentication scheme based

on chaotic fragile watermark. In Emerging Technologies for a Smarter World (CEWIT),

2012 9th International Conference & Expo on (pp. 1-6). IEEE.

 

Xiao, D., & Shih, F. Y. (2012). An improved hierarchical fragile watermarking scheme using

chaotic sequence sorting and subblock post-processing. Optics Communications,

285(10), 2596-2606.

 

Xie, S., Lawnizak, A. T., Lio, P., & Krishnan, S. (2013). Feature extraction by multi-scale

principal component analysis and classification in spectral domain. Engineering, 5(10),

268.

 

Xingyang, Z., & Jiyin, S. (2009, October). A novel clor image fragile watermarking based on the

extended channel. In Broadband Network & Multimedia Technology, 2009. ICBNMT'

09. 2nd IEEE International Conference on (pp. 422-428). IEEE.

 

Xu, H., Wang, J., & Kim, H. J. (2010). Near-optimal solution to pair-wise LSB matching via an

immune programming strategy. Information Sciences, 180(8), 1201-1217.

 

Yadav, M., & Dhankhar, A.(2015) Image Steganography Techniques: A Review. IJIRST, Vol. 2

 

Yaiprasert, C., Jaroensutasinee, K., and Jaroensutasinee, M., (2007). The pixel value

dataapproach for rainfall forecasting based on GOES-9 satellite image sequence analysis.

World Academy of Science, Engineering and Technology, 3(8), 186-191.

 

Yang, M., Kpalma, K., & Ronsin, J. (2008). A survey of shape feature extraction techniques.

Pattern Recognition, IN-TECH, pp.43-90, 2008.

 

Yogamangalam, R., & Karthikeyan, B. (2013). Segmentation techniques comparison in image

processing. International Journal of Engineering and Technology (IJET), 5(1), 307-313.

 

Yu, H., Yang, J., & Han, J. (2003, August). Classifying large data sets using SVMs with

hierarchical clusters. In Proceedings of the ninth ACM SIGKDD international conference

on Knowledge discovery and data mining (pp. 306-315). ACM.

 

Yu, L., Zhao, Y., Ni, R., & Li, T. (2010). Improved adaptive LSB steganography based on chaos

and genetic algorithm. EURASIP Journal on Advances in Signal Processing, 2010(1),

876946.

 

Yuan, X., Situ, N., & Zouridakis, G. (2008). Automatic segmentation of skin lesion images using

evolution strategies. Biomedical signal processing and control, 3(3), 220-228.

 

Zaidan, A. A. (2013). Anti-pornography algorithm based on multi-agent learning in skin detector

and pornography classifier (Doctoral dissertation, Multimedia University (Malaysia)).

 

Zaidan, A. A., Karim, H. A., Ahmad, N. N., Alam, G. M., & Zaidan, B. B. (2010). A new hybrid

module for skin detector using fuzzy inference system structure and explicit rules.

International Journal of Physical Sciences, 5(13), 2084-2097.

 

Zainal, A. (2011). An adaptive intrusion detection model for dynamic network traffic patterns

using machine learning techniques. Doctoral dissertation, Universiti Teknologi Malaysia,

Faculty of Computer Science and Information System.

 

Zhang, J., Zhang, Q., & Lv, H. (2013). A novel image tamper localization and recovery

algorithm based on watermarking technology. Optik-International Journal for Light and

Electron

 

Zhang, J., Zhang, Q., & Lv, H. (2013). A novel image tamper localization and recovery

algorithm based on watermarking technology. Optik-International Journal for Light and

Electron Optics, 124(23), 6367-6371.

 

Zhang, S., Li, X., Zong, M., Zhu, X., & Cheng, D. (2017). Learning k for knn classification.

ACM Transactions on Intelligent Systems and Technology (TIST), 8(3), 43.

 

Zhongqin, W., Lu, H., Yang, S., Qiang, Z., & Chaoxia, W. (2015, October). Study of digital

watermark based on chaos algorithm. In Cyberspace Technology (CCT 2015), Third

International Conference on (pp. 1-5). IET.

 

Zhongqin, W., Lu, H., Yang, S., Qiang, Z., & Chaoxia, W. (2015, October). Study of digital

watermark based on chaos algorithm. In Cyberspace Technology (CCT 2015), Third

International Conference on (pp. 1-5). IET.

 

Zhu, H. (2003). Medical image processing overview. University of Calgary.

 

Zhu, P., & Zhao, M. S. (2010, July). A chaotic system based watermarking algorithm for image

copyright protection. In Computer Science and Information Technology (ICCSIT), 2010

3rd IEEE International Conference on (Vol. 6, pp. 220-222). IEEE.

 

Zhu, Y., & Huang, C. (2012). An improved median filtering algorithm for image noise reduction.

Physics Procedia, 25, 609-616.

 

Zouridakis, G., Doshi, M., & Mullani, N. (2004, September). Early diagnosis of skin cancer

based on segmentation and measurement of vascularization and pigmentation in

nevoscope images. In Engineering in Medicine and Biology Society, 2004. IEMBS'04.

26th Annual International Conference of the IEEE (Vol. 1, pp. 1593-1596). IEEE.