i-manager Publications

i-manager's Journal on Image Processing (JIP)

Current Issue Vol. 11 Issue 2

Most Cited

Identification of Volcano Hotspots by using Resilient Back Propagation (RBP) Algorithm Via Satellite Images
Data Hiding in Encrypted Compressed Videos for Privacy Information Protection
Improved Video Watermarking using Discrete Cosine Transform
Contrast Enhancement based Brain Tumour MRI Image Segmentation and Detection with Low Power Consumption
Denoising of Images by Wavelets and Contourlets using Bi-Shrink Filter

Volume 6 Issue 1 January - March 2019

Research Paper

Optimized Video Compression Using Modified Intelligent Behaviour of Firefly Algorithm

H. A. Abdulkareem * , A. M. S. Tekanyi, I. Yau, K. A. Abu- Bilal, H. Adamu

*-*****Ahmadu Bello University, Zaria, Nigeria.

Abdulkareem, H. A., Tekanyi, A. M. S., Yau, I., Abu-Bilal, K. A.,& Adamu, H.(2019). Optimized Video Compression Using Modified Intelligent Behaviour of Firefly Algorithm. i-manager's Journal on Image Processing, 6(1), 1-8. https://doi.org/10.26634/jip.6.1.16363

Abstract

Transformation in mobile networks and multimedia communications make image and video compression important aspects of digital image processing. The main aim of image or video compression is to reduce the size of the image or video (redundancy) with little or no degradation of quality for an effective transmission and storage. This paper presents an optimized video compression using modified intelligent behavior of firefly algorithm. A total of six (four acquired and two benchmark) sample video data were used to implement the achieved technique. Frames were extracted from the video data and stored in the form of images in a buffer. Compression of the video frames was achieved by reducing the effect of pixel intensity with larger distance part. This was identified as one of the shortcomings with the Firefly Optimization Algorithm (FOA) method of image compression. In this paper, the impact of the modification was clearly shown using the Peak Signal to Noise Ratio (PSNR). The modification was achieved by including the root mean square in the standard equations of the FOA. In order to reduce the effect of pixel intensity with larger distance part, this was identified as one of the shortcomings. When the image samples were subjected to the (mFOA) compression technique, a same amount of improvement was achieved. Simulation results indicated that the mFOA technique outperformed the FOA method. The PSNR evaluation showed an improved reduction of frame size by 7.34%, 3.30%, 4.90%, and 5.75% for respective NAERLS1.avi, NAERLS2.avi, NTA1.avi, and NTA2.avi captured benchmark video frames and also 3.56% and 3.86% for respective video frames of Akiyo.avi and Forman.avi

References

[1]. Abdulkareem, H. A., Tekanyi, A. M. S., Yau, I., Abu- Bilal, K. A., & Adamu, H. A. (2018). Optimized Video Compression using Modified Intelligence Behaviour of Firefly Algorithm. 2^nd International Conference on Information and Communication Technology and its Applications (ICTA 2018) (pp. 357-363).

[2]. Abdullah, A., Deris, S., Mohamad, M. S., & Hashim, S. Z. M. (2012). A new hybrid firefly algorithm for complex and nonlinear problem. In Distributed Computing and Artificial Intelligence (pp. 673-680). Springer, Berlin, Heidelberg.

[3]. Apostolopoulos, T., & Vlachos, A. (2010). Application of the firefly algorithm for solving the economic emissions load dispatch problem. International Journal of Combinatorics, 2011.

[4]. Database: Images & Video Clips (2). (2006). Collected by the HDTV Group, July, 2006. xidian.edu.cn/vipsl/ database_Video.html

[5]. Farook, S., & Raju, P. S. (2013). Evolutionary hybrid genetic-firefly algorithm for global optimization. IJCEM International Journal of Computational Engineering & Management, 16(3), 37-45.

[6]. Hassanzadeh, T., & Meybodi, M. R. (2012, May). A new hybrid algorithm based on Firefly Algorithm and th cellular learning automata. In 20 Iranian Conference on Electrical Engineering (ICEE2012) (pp. 628-633). IEEE.

[7]. Hoon, C. Y. (2007). Cross-Colour Noise Reduction Algorithms for NTSC Signals. Conference Location: Las Vegas, NV, Publisher.

[8]. Horng, M. H., & Jiang, T. W. (2010, October). Multilevel image thresholding selection based on the firefly algorithm. In 2010 7^th International Conference on Ubiquitous Intelligence & Computing and 7^th International Conference on Autonomic & Trusted Computing (pp. 58-63). IEEE.

[9]. Srivatsava, P. R., Mallikarjun, B., & Yang, X. S. (2013). Optimal test sequence generation using firefly algorithm. Swarm and Evolutionary Computation, 8, 44-53.

[10]. Tilahun, S. & Ong, H. (2012). Modified Firefly Algorithm. Journal of Applied Mathematics, 1-12.

[11]. Yang, X. S. (2010). Firefly algorithm, Levy flights and global optimization. In Research and Development in Intelligent Systems XXVI (pp. 209-218). Springer, London.

[12]. Yu, S., Yang, S., & Su, S. (2013). Self-adaptive step firefly algorithm. Journal of Applied Mathematics, 2013,1-8.

[13]. Yu, S., Zhu, S., Ma, Y., & Mao, D. (2015). Enhancing firefly algorithm using generalized opposition-based learning. Computing, 97(7), 741-754.

[14]. Zhang, L., Fielding, B., Kinghorn, P., & Mistry, K. (2016, May). A vision enriched intelligent agent with image description generation. In Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems (pp. 1488-1490). International Foundation for Autonomous Agents and Multiagent Systems.

[15]. Zhang, L., Mistry, K., Jiang, M., Neoh, S. C., & Hossain, M. A. (2015). Adaptive facial point detection and emotion recognition for a humanoid robot. Computer Vision and Image Understanding, 140, 93-114.

[16]. Zhang, Y., Zhang, L., Neoh, S. C., Mistry, K., & Hossain, M. A. (2015). Intelligent affect regression for bodily expressions using hybrid particle swarm optimization and adaptive ensembles. Expert Systems with Applications, 42(22), 8678-8697.

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Research Paper

Alzheimer’s Stage Classification using SVM Classifier using Brain MRI Texture Features

Shaik Basheera* , M. Satya Sai Ram**

* Department of Electronics and Communication Engineering, Acharya Nagarjuna University College of Engineering, Acharya Nagarjuna University, Guntur, India.

**Department of Electronics and Communication Engineering, RVR & JC College of Engineering, Guntur, Andhra Pradesh, India.

Basheera, S., & Ram, M. S. S. (2019). Alzheimer’s Stage Classification using SVM Classifier using Brain MRI Texture Features. i-manager's Journal on Image Processing, 6(1), 9-16. https://doi.org/10.26634/jip.6.1.15742

Abstract

This paper deals with the Brain disorder caused due to dementia, where the brain size gets effected and reduces its volume. Estimating the grade of Alzheimer's is a challenging task. Spatial texture information collected from T2 Weighted MRI images are used to perform classification and validation. The authors use 54 Brain MRI Slices, Gray Level Cooccurrence matrix is used to extract the attributes, and those are used to train the classifiers. On comparing Support Vector Machine (SVM) with Naïve Bayes classifier and KNN, SVM gives good classification accuracy of 98.1%. The classifiers classify the MRI into AD, MCI, and CN. Five independent images are collected from the Internet sources, by testing those images using SVM and correlating with clinical data of those images, it achieves 100% accuracy.

References

[1]. Ardekani, B. A., Bermudez, E., Mubeen, A. M., & Bachman, A. H. (2017). Prediction of incipient Alzheimer's disease dementia in patients with mild cognitive impairment. Journal of Alzheimer's Disease, 55(1), 269- 281.

[2]. Bobinski, M., Wegiel, J., Wisniewski, H. M., Tarnawski, M., Bobinski, M., Reisberg, B., ... & Miller, D. C. (1996). Neurofibrillary pathology-correlation with hippocampal formation atrophy in Alzheimer disease. Neurobiology of Aging, 17(6), 909-919.

[3]. Brettschneider, S., Morgenthaler, N.G., Teipel, S.J., Fischer-Schulz, C., Bürger, K., Dodel, R., Du, Y., Möller, H.J., Bergmann, A., & Hampel, H., (2005). Decreased serum amyloid β1-42 autoantibody levels in Alzheimer's disease, determined by a newly developed immuno- precipitation assay with radiolabeled amyloid β1-42 peptide. Biol. Psychiatry. 57 (7), 813-816. doi:10.1016/j.biopsych.2004.12.008

[4]. Cedazo-Minguez, A., & Winblad, B. (2010). Biomarkers for Alzheimer's disease and other forms of dementia: clinical needs, limitations and future aspects. Experimental Gerontology , 45 (1), 5-14. doi:10.1016/j.exger.2009.09.008

[5]. Dimitriadis, S. I., Liparas, D., & Tsolaki, M. N. (2018). Random forest feature selection, fusion and ensemble strategy: Combining multiple morphological MRI measures to discriminate among healhy elderly, MCI, cMCI and alzheimer's disease patients: From the alzheimer's disease neuroimaging initiative (ADNI) database. Journal of Neuroscience Methods, 15(302), 14-23.

[6]. Farouk, Y., Rady, S., & Faheem, H. (2018, April). Statistical features and voxel-based morphometry for alzheimer's disease classification. In 2018 9^th International Conference on Information and Communication Systems (ICICS) (pp. 133-138). IEEE.

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[8]. Giraldo, D. L., García Arteaga, J. D., Cárdenas Robledo, S., & Romero, E. (2018). Characterization of brain anatomical patterns by comparing region intensity distributions: Applications to the description of Alzheimer's disease. Brain and Behavior, 8(4).

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[11]. Ho, L., Fivecoat, H., Wang, J., & Pasinetti, G. M. (2010). Alzheimer's disease biomarker discovery in symptomatic and asymptomatic patients: Experimental approaches and future clinical applications. Experimental Gerontology, 45(1), 15-22. doi:10.1016/ j.exger.2009.09.007

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[14]. Liu, J., Wang, J., Hu, B., Wu, F. X., & Pan, Y. (2017). Alzheimer's disease classification based on individual hierarchical networks constructed with 3-D texture features. IEEE Transactions on Nanobioscience, 16(6), 428-437.

[15]. Liu, X., Tosun, D., Weiner, M. W., & Schuff, N. (2013). Locally linear embedding (LLE) for MRI based Alzheimer's disease classification. Neuroimage, 83, 148-157.

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[17]. Martinez-Murcia, F. J., Górriz, J. M., Ramírez, J., Segovia, F., Salas-Gonzalez, D., & Castillo-Barnes. (2017, July). Evaluating Alzheimer's disease diagnosis using texture analysis. In Annual Conference on Medical Image Understanding and Analysis (pp. 470-481). Springer, Cham.

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[19]. Sprott, R. L. (2010). Biomarkers of aging and disease: introduction and definitions. Experimental Gerontology, 45(1), 2-4. Retrieved from https://doi.org/10.1016/ j.exger.2009.07.008

[20]. Telagarapu, P., Mohanty, B., & Anandh, K. R. (2018, July). Analysis of Alzheimer Condition in T1-Weighted MR Images Using Texture Features and K-NN Classifier. In 2018 International CET Conference on Control, Communication, and Computing (IC4) (pp. 331-334). IEEE.

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Research Paper

Skin Texture Recognition through Image Processing

Sukhdeep Sharma * , Aayushi Dubey**

*-**Department of Computer Science and Engineering, Manav Rachna International Institute of Research and Studies, Haryana, India.

Sharma, S., & Dubey, A. (2019). Skin Texture Recognition through Image Processing. i-manager's Journal on Image Processing, 6(1), 17-25. https://doi.org/10.26634/jip.6.1.15560

Abstract

Image processing is done to get a better version of an image or an enhanced or filtered image. It is used to extract important information from the image. A raw image is converted into a digital image by undergoing various processes for which proper algorithms and mechanisms are defined. To further investigate on an image, we can perform texture analysis on it. Texture recognition is also an important characteristic of image processing. Both texture recognition and image processing have a variety of uses in the medicine field. In this paper, the authors have discussed about how we can detect the texture of our skin and use it further to test the suitability of skin products that we use daily.

References

[1]. Aruta, C. L., Calaguas, C. R., Gameng, J. K., Prudentino, M. V., Anthony, A., & Lubaton, C. J. (2015). Mobile-based medical assistance for diagnosing different types of skin diseases using case-based reasoning with image processing. IJ CI, 3(3), 115-118.

[2]. Bora, D. J. (2017). Importance of image enhancement techniques in color image segmentation: A comprehensive and comparative study. Indian J. Sci. Res., 15(1), 115-131.

[3]. Deshmukh, A. S., & Lokhande, K. P. (2016). Human Skin Texture Analysis using GLCM Technique. International Journal of Ethics in Engineering & Management Education, 3(9), 10-12.

[4]. Dubey, A., Sharma, S., Malik, S., & Jain, P. (2018). A Detailed study of Digital Image Processing. The IIOAB Journal, 9(2), 33-42.

[5]. Fekri-Ershad, S., Saberi, M., & Tajeripour, F. (2012). An innovative skin detection approach using color based image retrieval technique. arXiv preprint arXiv:4(3), 1207.1551.

[6]. Jakhar, D., Misra, A., & Dabas, S. (2018). Myiasis under dermatoscope: The hidden story. Indian Dermatology Online Journal, 9(3), 220-221.

[7]. Karthikeyani, V., Duraiswamy, K., & Kamalakkannan, P. (2007). Conversion of gray-scale image to color image with and without texture synthesis. IJCSNS, 7(4), 11-16.

[8]. Kaur, D., & Sandhu, P. (2013). Human skin texture analysis using image processing techniques. Int. J. Sci. Res. (IJSR), 17-20.

[9]. Kumar, V., & Nanda, M. (2008). Image Processing in Frequency Domain using Matlab®: A Study for Beginners.

[10]. Lavania, K. K., & Kumar, R. (2011). A comparative study of image enhancement using histogram approach. Int. J. Comput. Appl., 32(5), 1-6.

[11]. Prätorius, M., Scherzinger, A., & Hinrichs, K. (2015, September). SkInteract: An on-body interaction system based on skin-texture recognition. In IFIP Conference on Human-Computer Interaction (pp. 425-432). Springer, Cham.

[12]. Sparavigna, A., & Marazzato, R. (2010). An image processing analysis of skin textures. Skin Research and Technology, 16(2), 161-167.

[13]. Xie, J., Zhang, L., You, J., Zhang, D., & Qu, X. (2012). A study of hand back skin texture patterns for personal identification and gender classification. Sensors, 12(7), 8691-8709.

[14]. Yadav, N., Narang, V. K., & Shrivastava, U. (2016). Skin diseases detection models using image processing: A survey. International Journal of Computer Applications, 137(12), 34-39.

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Research Paper

Iris Recognition based on Optimized Orthogonal Wavelet and Local Tetra Pattern (OOWLTrP) using Neural Network

Nuzhat F. Shaikh*

Professor and Head, Department of Computer Engineering, M. E. S. College of Engineering, Pune, Maharastra, India.

Shaikh, N. F. (2019). Iris Recognition based on Optimized Orthogonal Wavelet and Local Tetra Pattern (OOWLTrP) using Neural Network. i-manager's Journal on Image Processing, 6(1), 26-37. https://doi.org/10.26634/jip.6.1.14735

Abstract

This paper proposes a novel feature descriptor using Optimized Orthogonal Wavelets and Local Tetra Patterns OOWLTrP. Texture features are extracted by using Local Tetra Pattern (LTrP) and wavelet features are extracted through optimized orthogonal wavelet. Eye image from various databases, such as CASIA, MMUI, and UBRIS are first preprocessed to remove salt and pepper noise. Later, the iris is segmented from the rest of the eye image. Features are then extracted by using the proposed method, which combines the goodness of local patterns and orthogonal wavelets. Feed Forward Back Propagation Neural Network (FFBNN) is used for classification of images. During training, the weights of FFBNN are optimized using the Adaptive Central Force Optimization (ACFO). The well trained FFBNN-ACFO is further used for classification of iris images. It has been observed that, there is considerable improvement in accuracy and validation time of the system. The increase in accuracy is due to the fact that, LTrP extracts information from four directions as compared to LBP (Local Binary Pattern), LDP (Local Derivative Pattern), and LTP (Local Ternary Pattern). Coefficients of the orthogonal wavelet are optimization by genetic operators, this adds to the improvement in accuracy and reduction in validation time.

References

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[2]. de Martin-Roche, D., Sanchez-Avila, C., & Sanchez- Reillo, R. (2001, October). Iris recognition for biometric identification using dyadic wavelet transform zero-crossing. In Proceedings IEEE 35^th Annual 2001 International Carnahan Conference on Security Technology (Cat. No. 01CH37186) (pp. 272-277). IEEE.

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[4]. Guo, Z., Zhang, L., & Zhang, D. (2010). A completed modeling of local binary pattern operator for texture classification. IEEE Transactions on Image Processing, 19(6), 1657-1663.

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[7]. Ma, L., Tan, T., Wang, Y., & Zhang, D. (2004). Efficient iris recognition by characterizing key local variations. IEEE Transactions on Image Processing, 13(6), 739-750.

[8]. Mäenpää, T., & Pietikäinen, M. (2005). Texture analysis with Local Binary Patterns. In Handbook of Pattern Recognition and Computer Vision (pp. 197-216).

[9]. Murala, S., Maheshwari, R. P., & Balasubramanian, R. (2012). Local Tetra Patterns: A new feature descriptor for content-based image retrieval. IEEE Transactions on Image Processing, 21(5), 2874-2886.

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[11]. Ojala, T., Pietikäinen, M., & Mäenpää, T. (2002). Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis & Machine Intelligence, (7), 971-987.

[12]. PVSSR, C. M. (2016). Dimensionality reduced local directional pattern (DR-LDP) for face recognition. Expert Systems with Applications, 63, 66-73.

[13]. Sarhan, A. M. (2009). Iris Recognition using Discrete Cosine Transform. Journal of Computer Science, 5(5), 369-373.

[14]. Shaikh, N. F., & Doye, D. D. (2013a). An adaptive iris recognition system with aid of local histogram and optimized FFBNN-AAPSO. International Review on Computers and Software (IRECOS) [Online], 8(12).

[15]. Shaikh, N. F., & Doye, D. D. (2013b). Improving the Accuracy of Iris Recognition system using Neural Network and Particle Swarm Optimization. International Journal of Computer Applications, 79(3).

[16]. Shaikh, N. F., & Doye, D. D. (2016). An Adaptive Central Force Optimization (ACFO) and Feed Forward Back Propagation Neural Network (FFBNN) based iris recognition system. Journal of Intelligent & Fuzzy Systems, 30(4), 2083-2094.

[17]. Sibai, F. N., Hosani, H. I., Naqbi, R. M., Dhanhani, S., & Shehhi, S. (2011). Iris recognition using artificial neural networks. Expert Systems with Applications, 38(5), 5940- 5946.

[18]. Susitha, N., & Subban, R. (2018). A Novel Iris based Security Policy using LDP and Ensemble Classification. International Journal of Current Research and Modern Education (IJCRME), 3(1), 325-333.

[19]. Tan, X., & Triggs, W. (2010). Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Transactions on Image Processing, 19(6), 1635-1650.

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Survey Paper

Performance Analysis of Copy-Move Forgery Detection Techniques

Gulivindala Suresh * , Chanamallu Srinivasa Rao **

*_**Department of Electronics & Communication Engineering, JNTUK University College of Engineering, Kakinada, Andhra pradesh, India.

Suresh, G., & Rao, C. S. (2019). Performance Analysis of Copy-Move Forgery Detection Techniques. i-manager's Journal on Image Processing, 6(1), 38-43. https://doi.org/10.26634/jip.6.1.15925

Abstract

Copy Move Forgery (CMF) is a manipulation process, where a part of the image is copied and moved to another region in the same image. The advanced growth in technology and photo-editing software lead to the malicious manipulation of images. Distribution of such tampered images through high speed digital networks and social media is also increased, which leads to the incredibility of the images and the underlying information. Hence, it is much demanded to develop, evaluate, and propose CMF detection techniques. CMF detection can be achieved either by keypoint approach or block-based approach. In this paper, performance of block-based and keypoint based CMF detection and localization techniques are analyzed.

References

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[2]. Amerini, I., Ballan, L., Caldelli, R., Del, A., Del, L., & Serra, G. (2013). Copy-Move forgery detection and localization by means of Robust Clustering with J-Linkage. Signal Processing: Image Communication, 28(6), 659- 669.

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[13]. Jaberi, M., Bebis, G., Hussain, M., & Muhammad, G. (2014). Accurate and robust localization of duplicated region in copy- move image forgery. Machine Vision and Applications, 25(2), 451-475. https://doi.org/10.1007/ s00138-013-0522-0

[14]. Jing, L., & Shao, C. (2012). Image copy- move forgery detecting based on local invariant feature. Journal of Multimedia, 7(1), 90-97. https://doi.org/ 10.4304/jmm.7.1.90-97

[15]. Kaur, A., & Richa, S. (2013). Copy-Move Forgery Detection using DCT and SIFT. International Journal of Computer Applications, 70(7), 30-34.

[16]. Kumar, S., Desai, J. V., & Mukherjee, S. (2015). Copy Move Forgery detection in Contrast Variant Environment using Binary DCT Vectors. International Journal of Image, Graphics & Signal Processing, 7(6), 38-44. https://doi.org/ 10.5815/ijigsp.2015.06.05

[17]. Kuznetsov, A., & Myasnikov, V. (2017). A new copymove forgery detection algorithm using image preprocessing procedure. Proceedia Engineering, 201, 436-444. https://doi.org/.1037//0033-2909.I26.1.78

[18]. Lee, J. C. (2015). Copy-move image forgery detection based on Gabor magnitude. Journal of Visual Communication and Image Representation, 31, 320-334. https://doi.org/10.1016/j.jvcir.2015.07.007

[19]. Li, J., Li, X., Yang, B., & Sun, X. (2015). Segmentationbased image copy-move forgery detection scheme. IEEE Transactions on Information Forensics and Security, 10(3), 507-518. https://doi.org/10.1109/TIFS.2014. 2381872

[20]. Li, L., Li, S., Zhu, H., Chu, S. C., Roddick, J. F., & Pan, J. S. (2013). An efficient scheme for detecting copy-move forged images by local binary patterns. Journal of Information Hiding and Multimedia Signal Processing, 4(1), 46-56.

[21]. Lin, Z., He, J., Tang, X., & Tang, C. K. (2009). Fast , automatic and fine-grained tampered JPEG image detection via DCT coefficient analysis. Pattern Recognition, 42(11), 2492-2501. https://doi.org/10.1016/ j.patcog.2009.03.019

[22]. Lin, G., Chang, M. K., & Chen, Y. L. (2011). A Passive- Blind Forgery Detection Scheme based on Content. Adaptive Quantization Table Estimation, 21(4), 421–434.

[23]. Liu, G., Wang, J., Lian, S., & Wang, Z. (2011). A passive image authentication scheme for detecting region-duplication forgery with rotation. Journal of Network and Computer Applications, 34(5), 1557-1565. https://doi.org/10.1016/j.jnca.2010.09.001

[24]. Mahmood, T., Nawaz, T., Irtaza, A., Ashraf, R., Shah, M., & Mahmood, M. T. (2016). Copy-Move Forgery Detection technique for Forensic Analysis in Digital Images. Mathematical Problems in Engineering, 2016, Article ID 8713202. https://doi.org/10.1155/2016/8713202

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i-manager's Journal on Image Processing (JIP)

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H. A. Abdulkareem * , A. M. S. Tekanyi, I. Yau, K. A. Abu- Bilal, H. Adamu

* Department of Electronics and Communication Engineering, Acharya Nagarjuna University College of Engineering, Acharya Nagarjuna University, Guntur, India.

**Department of Electronics and Communication Engineering, RVR & JC College of Engineering, Guntur, Andhra Pradesh, India.