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

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Multi-Lingual Character Recognition and Extraction using Recurrent Neural Networks
A Novel Approach to Smart Autonomous Monitoring of Indoor Plant Health Based on Leaf Color
Edge Preserving Image Denoising with Deep Networks and Ensemble Convolutional Neural Network Architectures
Neural Network Approach to Detect Renal Calculi
Enhancing Fabric Quality Control: Implementing Real-Time Defect Detection with Image Processing Techniques and Arduino

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Volume 1 Issue 3 July - September 2014

Article

A Review of Intensity and Model Based Segmentation Methods for MRI

R. Neela* , R. Kalaimagal**

* Research Scholar, Manonmaniam Sundaranar University, Tirunelveli, India.

** Assistant Professor, Department of Computer Science, Govt. Arts College for Men (Autonomous), Chennai, India.

Neela, R., and Kalaimagal, R. (2014). A Review of Intensity and Model Based Segmentation Methods for MRI. i-manager’s Journal on Image Processing, 1(3), 1-10. https://doi.org/10.26634/jip.1.3.2958

Abstract

Segmentation of various structures is a crucial step in the diagnosis and treatment of various diseases. Various imaging techniques such as X-ray, CT, (Computer Tomography), DTI (Diffusion Tensor Image), MRI (Magnetic Resonance Imaging), FMRI (Functional Magnetic Resource Imaging), PET (Positron Emission Tomography), SPECT (Single Photon Emission Computed Tomography), etc. are used in the treatment of diseases. Selection of the segmentation method depends on the modality used and the structures to be segmented. Accurate segmentation of various structures and computation of volume of tissues are required in the treatment of various diseases. In this paper, the authors present an overview of various segmentation techniques used in MRI analysis and their pros and cons. Also, the performance measures of some methods are evaluated.

References

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[28]. Xian Fan, Yiqiang Zhan and Gerardo Hermosillo Valadez. (2009). “A Comparison study of atlas based image segmentation and the advantage of multi-atlas based on shape clustering”. Proc. SPIE 7259, Medical Imaging. doi:10.1117/12.814157.

[29]. Aljabar, P., Heckemann, R., Hammers, A., Hajnal, J.V., and Rueckert, D. (2009). “Multi-Atlas Based Segmentation of Brain Images: Atlas Selection and Its Effect on Accuracy”. NeuroImage, Vol.46(3), pp.726- 739.Retrived from http://www.doc.ic.ac.uk/~pa100/pubs/ aljabarNeuroImage 2009-selection.pdf.

[30]. Rohlfing, T., Robert Brandt, Randolf Menzel,and Calvin R. Maurer, Jr. (2004). “Evaluation of atlas selection strategies for atlas-based image segmentation with application to confocal microscopy images of bee brains ”. NeuroImage, 21(4), 1428–1442, doi: 10.1016/j.neuroimage.2003.11.010.

[31]. Zitova, B., and Flusser, J. (2003). “Image registration methods: a survey”. Image and Vision Computing, Vol.21(11), pp.977–1000.

[32]. Warfield, S. K., Zou, K.H., and Wells, W.M. (2004). “Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation”. IEEE Transactions on Medical Imaging, Vol.23(7), pp. 903–921.

[33]. Rohlfing and C. R. Maurer Jr. (2007). “Shape-based averaging”, IEEE Transactions on Image Processing, Vol.16(1), pp.153–161.

[34]. M. R. Sabuncu, B. T. T. Yeo, K. Van Leemput, B. Fischl, and P. Golland, “A generative model for image segmentation based on label fusion,” IEEE Transactions on Medical Imaging, Vol. 29, No. 10, Article ID 5487420, pp. 1714–1729, 2010.

[35]. Hongzhi Wang, J., Suh, W., Das, S.R., Pluta, J.B., Craige, C., and Yushkevich, P.A. (2012). “Multi-Atlas Segmentation with Joint Label Fusion”. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(4), 611- 623. doi:10.1109/TPAMI.2012.143.

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[38]. Ruben Cardenes, Meritxell Bach, Ying-Veronica Chi, Ioannis Marras, Rodrigo de Luis, Mats Anderson, Peter Cashman and Matthieu Bultelle. (2007). “Multimodal Evaluation Method for Medical Image Segmentation”. Computer Analysis of Images and Patterns, 4673, pp 229-236.

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Research Paper

Noise Minimization in Images using Dual-Tree Complex Wavelet Transform

M. Arunkumar* , D. Leela Rani**

* Student, Department of Electronics and Communication Engineering, Sree Vidyanikethan Engineering College, Tirupathi, A.P., India.

** Associate Professor, Department of Electronics and Communication Engineering, Sree Vidyanikethan Engineering College, Tirupathi, A.P., India.

Arunkumar, M., and Rani, D.L., (2014). Noise Minimization in Images using Dual-Tree Complex Wavelet Transform. i-manager’s Journal on Image Processing, 1(3), 11-17. https://doi.org/10.26634/jip.1.3.2959

Abstract

This paper presents a novel way to reduce noise introduced by different image enhancement techniques. As the human visual system is highly sensitive to change in brightness, the proposed method is applied to the luma channel of both the non-enhanced and enhanced image. The basic assumption is that the non-enhanced image is either free of noise or noise is present but not perceivable. In order to avoid inappropriate assumptions on the statistical characteristics of noise, a different one is proposed. Also, it gives the importance of directional content in human vision and the analysis is performed through the Dual-Tree Complex Wavelet Transform (DTCWT). Compared to discrete wavelet transform, the DTCWT provides distinction of data directionality in the transform space. The standard deviation for each level of the transform of an non-enhanced image coefficients is computed and normalized across the six orientations of the DTWCT. The normalized said map is then used to shrink the coefficients of the enhanced image. The shrunk coefficients of the enhanced image are mixed according to data directionality, with the coefficients of non-enhanced image. Finally, the inverse transform provides the noise-reduced version. The proposed one thoroughly reduces the noise introduced by the enhancement methods and produces better improvement in PSNR of the image. In order to confirm the validity of the proposed method, a through numerical analysis of the results has been done.

References

[1]. M. Fierro, W.-J. Kyung, and Y.-H. Ha, (2012). “Dual-tree complex wavelet transform based denoising for random spray image enahcement methods,” in Proc. 6th Eur. Conf. Colour Graph., Imag. Vis., pp. 194–199.

[2]. N. G. Kingsbury, (1998). “The dual-tree complex wavelet transform: A new technique for shift invariance and directional filters,” in Proc. 8th IEEE Digit. Signal Process. Workshop, Aug. No. 86, pp. 1–4.

[3]. D. L. Donoho and I. M. Johnstone, (1994). “Threshold selection for wavelet shrinkage of noisy data,” in Proc. 16th Annu. Int. Conf. IEEE Eng. Adv., New Opportunities Biomed. Eng. Eng. Med. Biol. Soc., Vol. 1. No.5, pp. A24–A25.

[4]. N. G. Kingsbury, (1999). “Image Processing with complex wavelets,” Philos.Trans. Math. Phys. Eng. Sci., Vol. 357, No. 1760, pp. 2543–2560.

[5]. I. W. Selesnick, R. G. Baraniuk, and N. G. Kingsbury, (2005). “The dual-tree complex wavelet transform: A coherent framework for multiscale images, Signal and image Processing,” Vol. 22, No. 6, pp. 123–151, Nov.

[6]. S. Cai and K. Li. Matlab, Implementation of Wavelet Transforms. Polytechnic Univ. Brooklyn, Newyork Retrived from http://eeweb.poly.edu/iselesni / WaveletSoftware/index.html

[7]. L. Menten and M. I. Michaelis, (1913). Die kinetik der invertinwirkung, Biochem, Vol. 49, pp. 333–369.

[8]. M. Livingstone, (2002). Vision and Art: The Biology of Seeing. New York: Harry N. Abrams.

[9]. K. I. Naka and W. A. H. Rushton, (1966). “S-potential from colour units in the retina of fish (Cyprinidae),” J. Physiol., Vol. 185, No. 3, pp. 536–555.

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Research Paper

Comparison of PSNR of DCT, DWT and DTT using Telenuclear Medical Image

N. Harathi* , V. Meenakshi, C.H. Suneetha*

- Assistant Professor, Department of EIE, Sree Vidyanikethan Engineering College, Tirupati.

Associate Professor, Department of ECE, Vignana Bharathi Institute of Technology, Aushapur.

Harathi, N., Meenakshi, V., and Suneetha, C.H. (2014). Comparison of PSNR of DCT, DWT and DTT using Telenuclear Medical Image. i-manager’s Journal on Image Processing, 1(3), 23-32. https://doi.org/10.26634/jip.1.3.2961

Abstract

The proliferation of digitized media due to the rapid growth of networked multimedia systems has created an urgent need for copyright enforcement technologies that can protect copyright. In this paper implementation of three different watermarking algorithms in the frequency domain is presented. The first algorithm is based on the Discrete Cosine Transform (DCT), the second one is based on the Discrete Wavelet Transform (DWT) and the third algorithm is based on the Discrete Tchebichef Transform (DTT).Embedding the watermark is done by modifying the coefficients of the middle frequency band so that the visibility of the image and diagnosis capability will not be affected. All schemes are tested using images and the simulation results are compared and the comparison shows the best scheme.

References

[1]. Suzuki and M. Nakamura, (1997). "Internet in Nuclear Medicine", Annals of Nuclear Medicine.

[ 2 ] . General Nuclear Medicine (http://www radiologyinfo.org)

[3]. R. Schyndel, A. Tirkel and C. Osborne, (1994). "A Digital Watermark", Proceeding of IEEE International Conference on Image, Vol.2.

[4]. C. Wang, S. Tai and C. Shou, (2000). "Repeating Image Watermarking Technique by the Visual Cryptography", IEICE Trans. Fundamentals, Vol.E83.

[5]. I. Cox, M. Miller and J. Bloom, (2001). "Digital Watermarking". New York: Morgan Kaufmann.

[6]. A. Sakova, (2004). "DCT versus WHT for Fragile Image Watermarking", Journal of Electrical Engineering, Vol. 55.

[7]. S. Maity and M. Kundu, (2009). "DHT Domain Digital Watermarking With Low Loss In Image Information", Science Direct, AEU - International Journal of Electronics and Communications.

[8]. V. Licks and R. Jordan, (2000). "On Digital Image Watermarking Robust To Geometric Transformation", Proc. IEEE Int. Conf. Image Processing, Vol.3.

[9]. A. Wakatani, (2002). “Digital Watermarking for ROI Medical Images by using Compressed Signature Image” Proceedings of the 35th International Conference, 2043 – 2048

[10]. Yusuk Lim, Changsheng Xu, and David Dagan Feng, (2001). “ Web based Image Authentication using Invisible Fragile Watermarking” Pan-Sydney Area Workshop on Visual Information Processing.

[11]. Hemin Golpira and Habibollah Danvali (2009). “Reversible Blind Watermarking for Medical Images Based on Wavelet Histogram Shifting”, IEEE, 31-36

[12]. C.-T. Hsu, J.-L. Wu, (1999). "Hidden Digital Watermarks in Images", IEEE Transactions on Image Processing, Vol. 8, pp. 58-68.

[13]. Jingbing Li, Huaiqiang Zhang, Yen-wei Chen, (2012). “Robust Zero-Watermarking for Medical Image Based on DCT” Photonics and Optoelectronics (SOPO), Symposium, 21-23 May 2012, 1-4.

[14]. E. Ganic and A. M. Eskicioglu, (2004). “Secure DWTSVD Domain Image Watermarking: Embedding Data in All Frequencies,” ACM Multimedia and Security Workshop 2004, Magdeburg, Germany, September 20-21.

[15]. Akshya Kumar Gupta, Mehul S Raval, “A Robust and Secure Watermarking scheme based on Singular Value Decomposition” Sadhana, Volume 37, Issue 4 , pp 425- 440.

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Research Paper

New Lifting Wavelets based Medical Image Compression with Entropy Coding

A. Hazarathaiah* , B. Prabhakara Rao**

* Head, Department of Electronics and Communications, S V College of Engineering.

** Rector, Jawarhalal Nehru technological University, Kakinada.

Hazarathaiah, A., and Rao, B.P. (2014). New Lifting Wavelets based Medical Image Compression with Entropy Coding. i-manager’s Journal on Image Processing, 1(3), 33-39. https://doi.org/10.26634/jip.1.3.2963

Abstract

The main limitations on any communication system are “channel bandwidth” and “noise”. This paper deals with the data rate which is related to channel bandwidth. The real time images from any source will occupy large amount of space on a storage device. Hence it consumes maximum bandwidth for transmitting it and there are so many methods to reduce the image size and hence the bandwidth of the signal. This paper shows wavelet based compression methods. Discrete Wavelet Transform is a powerful tool for analyzing the signals. With the help of a thresholding function, it will be more useful in compression, but this method has some disadvantages like selectivity and shift invariance. So a much better method is proposed. In the proposed method a completely new set of wavelets were designed with lifting filters. Arithmetic coding is combined with proposed algorithm for reducing the size of images with less mean square error (MSE) at high compression ratio (CR).

References

[1]. Pennebaker, W. B., and Mitchell, J. L.( 1993) , “JPEG: Still image data compression standard,” in New York, NY: Van Nostrand Reinhold.

[2]. Wallace, G. K. (1991), “The JPEG still picture compression standard,” in Commun. ACM, Vol.34, pp.30-44.

[3]. ISO/IEC CD 13818-2 (1993), “Generic coding of moving pictures and associated audio.”

[4]. Al-Haj, (2007). “Combined DWT-DCT Digital Image Water- marking,” Journal of Computer Science, Vol. 3, No. 9, pp. 740-746.

[5]. Daubechies, (1990). 'The Wavelet Transform, Time- Frequency Localization and Signal Analysis', IEEE Trans. Info. Theory, 36, 961-1005.

[6]. Glen G. Langdon Jr., An introduction to arithmetic coding. IBM Journal of Research and Development, 28(2):135-149.

[7]. Ian H. Witten, Radford M. Neal, and John G. Cleary, (1984). Arithmetic coding for data compression, Communications of the ACM, 30(6):520- 540.

[8]. C. E. Shannon, (1948). A mathematical theory of communication, Bell System Technical Journal, 27:379- 423 and 623-656.

[9]. Normand Abramson, (1963). Information Theory and Coding. McGraw-Hill Book Company, Inc., New York, NY.

[10]. P.Schroder and W. Sweldens, (1995). “Spherical wavelets: Efficiently representing functions on the sphere,” in ACM SIGGRAPH, pp. 161– 172.

[11]. R.W. Buccigrossi, E.P. Simoncelli: (Dec. 1999). Image Compression via Joint Statistical Characterization in the Wavelet Domain, IEEE Transaction on Image Processing, Vol. 8, No. 12, pp. 1688 – 1701.

[12]. A. Hazarathaiah, Dr. B. PrabhakarRao, (December 2013). “A Novel Medical Image Compression using New Traditional Orthogonal Wavelets”, CIIT International Journal of Digital Image Processing.

[13]. D.M. Chandler, S.S. Hemami (2002). Additivity Models for Suprathreshold Distortion in Quantized Wavelet-coded Images, Human Vision and lectronic Imaging Conference, San Jose, CA, USA, SPIE Vol. 4662, pp. 105 – 118.

[14]. www.gehealthcare.com.

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

Current Issue Vol. 10 Issue 4

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Volume 1 Issue 3 July - September 2014

A Review of Intensity and Model Based Segmentation Methods for MRI

R. Neela* , R. Kalaimagal**

* Research Scholar, Manonmaniam Sundaranar University, Tirunelveli, India. ** Assistant Professor, Department of Computer Science, Govt. Arts College for Men (Autonomous), Chennai, India.

Neela, R., and Kalaimagal, R. (2014). A Review of Intensity and Model Based Segmentation Methods for MRI. i-manager’s Journal on Image Processing, 1(3), 1-10. https://doi.org/10.26634/jip.1.3.2958

Abstract

References

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Noise Minimization in Images using Dual-Tree Complex Wavelet Transform

M. Arunkumar* , D. Leela Rani**

* Student, Department of Electronics and Communication Engineering, Sree Vidyanikethan Engineering College, Tirupathi, A.P., India. ** Associate Professor, Department of Electronics and Communication Engineering, Sree Vidyanikethan Engineering College, Tirupathi, A.P., India.

Arunkumar, M., and Rani, D.L., (2014). Noise Minimization in Images using Dual-Tree Complex Wavelet Transform. i-manager’s Journal on Image Processing, 1(3), 11-17. https://doi.org/10.26634/jip.1.3.2959

Abstract

References

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Comparison of PSNR of DCT, DWT and DTT using Telenuclear Medical Image

N. Harathi* , V. Meenakshi**, C.H. Suneetha***

*-** Assistant Professor, Department of EIE, Sree Vidyanikethan Engineering College, Tirupati. *** Associate Professor, Department of ECE, Vignana Bharathi Institute of Technology, Aushapur.

Harathi, N., Meenakshi, V., and Suneetha, C.H. (2014). Comparison of PSNR of DCT, DWT and DTT using Telenuclear Medical Image. i-manager’s Journal on Image Processing, 1(3), 23-32. https://doi.org/10.26634/jip.1.3.2961

Abstract

References

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New Lifting Wavelets based Medical Image Compression with Entropy Coding

A. Hazarathaiah* , B. Prabhakara Rao**

* Head, Department of Electronics and Communications, S V College of Engineering. ** Rector, Jawarhalal Nehru technological University, Kakinada.

Hazarathaiah, A., and Rao, B.P. (2014). New Lifting Wavelets based Medical Image Compression with Entropy Coding. i-manager’s Journal on Image Processing, 1(3), 33-39. https://doi.org/10.26634/jip.1.3.2963

Abstract

References

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* Research Scholar, Manonmaniam Sundaranar University, Tirunelveli, India.

** Assistant Professor, Department of Computer Science, Govt. Arts College for Men (Autonomous), Chennai, India.

* Student, Department of Electronics and Communication Engineering, Sree Vidyanikethan Engineering College, Tirupathi, A.P., India.

** Associate Professor, Department of Electronics and Communication Engineering, Sree Vidyanikethan Engineering College, Tirupathi, A.P., India.

N. Harathi* , V. Meenakshi, C.H. Suneetha*

- Assistant Professor, Department of EIE, Sree Vidyanikethan Engineering College, Tirupati.

Associate Professor, Department of ECE, Vignana Bharathi Institute of Technology, Aushapur.

* Head, Department of Electronics and Communications, S V College of Engineering.

** Rector, Jawarhalal Nehru technological University, Kakinada.