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

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Identification of Volcano Hotspots by using Resilient Back Propagation (RBP) Algorithm Via Satellite Images
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Volume 3 Issue 1 January - March 2016 [Open Access]

Research Paper

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.

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.

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.

References

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[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.

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Research Paper

Hybrid Wavelet based Approach for Image De-Noising through PCA

Gunjan Seth* , Sukhvir Kaur, Jagdeep Singh*

* PG Scholar, Department of Computer Science and Engineering, CT Institute of Engineering Management & Technology, Jalandhar, India.

Assistant Professor, Department of Computer Science and Engineering, CT Institute of Engineering Management & Technology, Jalandhar, India.

* Assistant Professor, Department of Electronics and Communication Engineering, National Institute of Technology, Jalandhar, India.

Sethi, G., Kaur, S., and Singh, J. (2016). Hybrid Wavelet Based Approach For Image De-Noising Through PCA. i-manager's Journal on Image Processing, 3(1), 13-19.

Abstract

De-Noising is a crucial problem for various types of image in the digital image processing. The main objective is to be fade away the noise factor by transfiguring into realistic Image as well as safeguarding the real quality and structure of the Image. Much hardware equipment such as digital electronic devices may suffer some issues that are noisy and blurred images due to degradation in the quality of the visioning image. These noisy images and blur images come under the problem of less information about the working object in a capturing environment. In this paper, the De-Noising technique has been proposed at different standard deviation for each processed image to check that at what level of noise it may work. In this proposed technique, wavelet is applied to a Noisy Image and further on the decomposed sections, SPG-PCA is used for quality enhancement. It consists of two stages: image estimation by removing the noise and further refinement of the first stage. Noise is removed at the maximum extent in first stage and the application of NPG improves the visualization of the De-Noised Image. A different standard deviation helps to optimize the original image which is based on the De-Noising scheme using quality matrices. The proposed technique can also be applied on satellite images, television pictures, medical images, etc. In this research work optimized De-Noising matrices like PSNR, SSIM, Maximum Difference and Normalized Cross-Correlation for the Dataset. Experimental results show a much improved performance of the proposed filters in the presence of Gaussian noise that are analyzed and illustrated.

References

[1]. Wright, G. (1997). “Magnetic resonance imaging”. In IEEE Signal Process. Mag. Vol. 1, pp. 56–66.

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[3]. Manjón, J.V., Carbonell - Caballero, J., Lull, J.J., Garciá-Martí, G., Martí - Bonmatí, L., and Robles, M. (2008). “MRI denoising using Non-Local Means”. In Med. Image Anal., pp. 514–523.

[4]. Wiest-Daesslé, N., Prima, S., Coupé, P., Morrissey, S. P., and Barillo,C. (2008). “Rician noise removal by non-local means filtering for low signal-to-noise ratio MRI: applications to DT-MRI”. In Proceedings of MICCAI, pp. 171-179.

[5]. Manjón, J.V., Coupé, P., Martí - Bonmatí, L., Collins, D.L., and Robles, M. (2010), “Adaptive non local means denoising of MR images with spatially varying noise levels”. In J. Magn. Reson. Imaging, pp. 192–203.

[6]. Sijbers, J., den Dekker, A.J., Van der Linden, A., Verhoye, M., and Van Dyck, D. (1999). “Adaptive anisotropic noise filtering for magnitude MR data”. In Magn. Reson. Imaging, pp. 1533–1539.

[7]. Samsonov, A.A., and Johnson, C.R, (2004). ”Noise adaptive nonlinear diffusion filtering of MR images with spatially varying noise levels”. In Magn. Reson. Med. Vol. 52, pp. 798–806.

[8]. Perona, P., and Malik J, (1990). “Scale space and edge detection using anisotropic diffusion”. In IEEE Trans. Pattern Anal. Mach. Intell. Vol. 12, pp. 629–639.

[9]. You, Y.L., and Kaveh M, (2000). “Fourth order partial differential equations for noise removal”. In IEEE Trans. Image Process., Vol. 9, pp. 1723–1730.

[10]. Lysaker, M., Lundervold, A., and Tai, X.C, (2003). “Noise removal using fourth-order partial differential equation with applications to medical magnetic resonance images in space and time”. In IEEE Trans. Image Process, Vol. 12, pp. 1579–1590.

[11]. Jyotsna Patil and Sunita Jadhav, (2013). “A comparative study of Image Denoising Techniques”. In International Journal of Innovative Research in Science, Engineering and Technology, Vol. 2, No. 3.

[12]. Monika Raghav and Sahil Raheja, (2014). “Image Denoising Techniques – Literature Review”. In International Journal of Engineering and Computer Science, Vol. 3, No. 5.

[13]. Lei Zhang, Weisheng Dong, David Zhang, and Guangming Shi, (2010). “Two-stage image denoising by principal component analysis with local pixel grouping”. In Elsevier, Vol. 43, No. 4, pp. 1531-1549.

[14]. Yongqin Zhang, Jiaying Liu, Mading Li, and Zongming Guo, (2013).“Joint image denoising using adaptive principal component analysis and self-similarity”. In Elsevier, Vol. 259, pp. 128-141.

[15]. J. Gunjan Sethi, Sukhvir Kaur, and Jagdeep Singh, (2016). “Hybrid Multistage Global Denoising Approach using PCA for standard Images”. IJSRD - International Journal for Scientific Research and Development, Vol. 3, No. 1, pp. 426-428.

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Research Paper

Multispectral Image Compression with High Resolution Improved SPIHT for Testing Various Input Images

V. Bhagya Raju,* , K. Jaya Sankar, C. D. Naidu*, Srinivas Bachu****

* Research Scholar, Department of Electronics and Communication Engineering, JNTU Hyderabad, Telangana, India.

Professor and Head, Department of ECE , Vasavi College of Engineering, Hyderabad, Telangana, India.

* Principal, VNR Vignana Jyothi Institute of Enineering & Technology, Hyderabad, Telangana, India.

**** Associate Professor, Department of ECE, Guru Nanak Institutions Technical Campus, Telangana, India.

Raju, V.B., Sankar, K.J., Naidu, C.D., and Bachu, S. (2016). Multispectral Image Compression With High Resolution Improved Spiht For Testing Various Input Images. i-manager's Journal on Image Processing, 3(1), 20-28.

Abstract

Due to the current development of Multispectral sensor technology, the use of Multispectral images has become more and more popular in recent years in remote sensing applications. This paper exploits the spectral and spatial redundancies that exist in different bands of multispectral images and effectively compresses these redundancies by means of a lossy compression method while preserving the crucial and vital spectral information of objects that prevails in the multispectral bands. In this paper, interpolated super resolution transform based DWT with Improved SPIHT algorithm for various multispectral datasets has been proposed. The proposed algorithm, a lossy multispectral image compression method yields better performance results for PSNR and Compression Ratio with sym8 wavelet when compared with previous well-known compression methods and existing discrete wavelets.

References

[1]. M.J. Weinberger, G. Seroussi, and G. Sapiro, (2000). “The LOCO-I lossless image compression algorithm: Principles and standardization into JPEG-LS”. IEEE Trans. Image Processing, Vol.12, pp. 1309-1324.

[2]. Francesco Rizzo, Bruno Carpentieri, Giovanni Amott and Jame A. Storer, (2005). “Low-Complexity Lossless Compression of Hyperspectral Imagery via Linear Prediction”. IEEE Signal Processing Letters, Vol. 12, No. 2.

[3]. Wu Zheng and He Mingyi, (2003). “Multispectral imagery compression by hybrid DWT and partitioning DPCM”. Journal of Electronics and Information Technology, Vol.125.

[4]. Sonal and Dinesh Kumar. ”A study of various Image compression techniques”. Guru Jhmbheswar University of Science and Technology, Hisar.

[5]. Marta Mrak and Sonia Grig. ”Picture quality Measures in image compression systems”. EUROCON 2003 Ljubljana, Slovenia.

[6]. Faisal Zubir Quereshi, “Image Compression using Wavelet Transform”.

[7]. Mahesh S. Chavan, Nikos Mastorakis, and Manjusha N. Chavan, ”Implementation of SYMLET Wavelets to Removal of Gaussian Additive Noise from Speech Signal”. ACM Digital Library, pp. 37-41.

[8]. Mohammed A. Salem, Nivin Ghamry, and Beate Meffert, (2009). ”Daubechies versus Biothogonal Wavelets for Moving Object Detection in Traffic Monitoring Systems”. Berlin Professor Endes Inst, For Informatik.

[9]. Xiaoli Tang and William A. Pearlman. (2005). “Hyperspectral Data Compression Three-Dimensional Wavelet-Based Compression”. Chapter in Hyperspectral Images, Kluwer Academic Publishers.

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[13]. Jian Sun, Jian Sun and Heung-Yeung, (2011). “Gradient Profile Prior and Its Applications in Image Super- Resolution and Enhancement”. IEEE TIP, Vol. 20, pp 1529- 1542.

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[16]. K.Sayood, (2000). Introduction to Data Compression. nd 2 Edition, Academic Press, Morgan Kaufman Publishers.

[17]. Yumnam Kirani Singh. “ISPIHT-Improved SPIHT “A simplified and efficient subband coding scheme”. For Development of Advanced Computing Plot: E-2/1, Block GP, Sector V, Salt Center Lake Electronics Complex.

[18]. V. Bhagya Raju, K. Jaya Sanhar, C. D. Naidu and Srinivas Bachu, (2016). “Multispectral Image Compression for various band images with High Resolution Improved DWT SPIHT”. International Journal of Signal Processing, Image Processing and Pattern Recoginition, Vol. 9, No. 2, pp. 271-286.

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Research Paper

Analysis of Iris Segmentation using Circular Hough Transform and Daughman's Method

Divya Ann Roy* , Urmila S. Soni**

* PG Scholar, Department of Electronics and Communication Engineering, CSIT, Durg, Chhattisgarh, India.

** Associate Professor, Department of Electronics and Communication Engineering, CSIT, Durg, Chhattisgarh, India.

Roy, D.A., and Soni, U.S. (2016). Analysis of Iris Segmentation using Circular Hough Transform and Daughman's Method. i-manager’s Journal on Image Processing, 3(1), 29-36.

Abstract

Iris recognition is a special type of biometric system, which is used to identify a person by analyzing the patterns in the iris. It is used to recognize the human identity through the textural characteristics of one's iris muscular patterns. Although eye colour is dependent on heredity, iris is independent even in the twins. Out of various biometrics such as finger and hand geometry, face, ear and voice recognition, iris recognition has been proved to be one of the most accurate and reliable biometric modalities because of its high recognition. Iris recognition involves 5 major steps. Firstly, image acquisition is done in which the image is captured by a high resolution camera, then the iris and the pupillary boundary are extracted out from the whole eye image, which is called segmentation. After segmentation, the circular dimension is converted to a fixed rectangular dimension which is called normalization. From this normalised image, the feature is extracted from Gabor filter, DFT, FFT, etc. At last, the iris code is matched using Hamming distance and Euclidean method. This project focuses on iris segmentation. Iris segmentation is the most important part in the iris recognition process because the areas that are wrongly considered as the iris regions would corrupt the biometric templates resulting in a very poor recognition [16]-[21]. The main objective of iris segmentation is to separate the iris region from the pupil and sclera boundaries. There are various methods for segmenting the iris from an eye image to give a best segmented result. In this project, iris segmentation is done using Daugman's integro differential method and Circular Hough Transform to find out the pupil and the iris boundaries. Iris images are taken from the CASIA V4 database, and the iris segmentation is done using Matlab software where iris and pupilary boundaries are segmented out. The experimental result shows that 84% accuracy is obtained by segmenting the iris by Circular Hough Transform and 76% accuracy is obtained by segmenting the iris through Daughman's method. It is concluded that, the Circular Hough Transform method of iris recognition is more accurate than the Daughman's method.

References

[1]. Ahmad Ayatollahi and Seyedzade Seyed Mohammad (2010). “A Novel Iris Recognition System Based th on Active Contour”. 17 Iranian Conference of Biomedical Engineering (ICBME2010).

[2]. Anawar S, Ayop Z and M. Manaf, (2012). “Iris Segmentation Analysis using Integro-Differential Operator and Hough Transform in Biometric System”. Journal of Telecommunication, Electronic and Computer Engineering, Vol. 4.

[3]. Adegoke, B. O, Omidiora, E. O, Falohun, S. A. and Ojo, J.A. (2013). “Iris Segmentation: A survey”. International Journal of Modern Engineering Research, Vol.3, No. 4.

[4]. Basu Somak and Dubey Maheedhar, (2013). “Daughman’s Algorithm Method For Iris Recognition: A Biometric Approach”. International Journal of Emerging Technology and Advanced Engineering, Vol. 2, No. 6.

[5]. Bendale Amit and Gupta Phalguni, (2012). “Iris Segmentation using an Improved Hough Transform”. Springer 2012.

[6]. Chawla Sunil and Oberoi Ashish, (2011). “A Robust Segmentation Method for Iris, Recognition”. International Journal of Advanced Research in Computer Science, Vol. 2, No. 5.

[7]. Chen Yen, Chang Han, Andrim Jean and Rishe N. (2009). “A highly accurate computationally efficient approach for unconstrained iris segmentation”. Elsevier.

[8]. Cherabit Noureddin, Chelali Fatma Zohra and Djeradi Amar, (2012). “Circular Hough Transform for Iris localization”. Science and Technology.

[9]. Daugman J, (2007). “New Methods in Iris Recognition”. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, Vol. 37, No. 5.

[10]. Dubey R. B and Madan Abhimanyu, (2014). Iris Localization using Daugman's Intero-Differential Operator, Vol. 93, No 3.

[11]. Djoumessi Maeva, (2012). “Iris Segmentation using Daughman's Intro-Differential Operator”. Conference Paper, University of North Carolina, Chapel Hill.

[12]. Dobeš M, Martineka M, Skoupila D, Dobešováb Z, and Pospíšilc J, (2005). “Human eye localization using the modified Hough transform”. Elsevier.

[13]. Gupta Rajeev and Kumar Ashok, (2013). “An Effective Segmentation Technique for Noisy Iris Images”. International Journal of Application or Innovation in Engineering & Management, Vol. 2, No. 12.

[14]. Ito Y, Ohyama W, Wakabayashi T, and Kimura T, (2012). “Detection of Eyes by Circular Hough Transform and st Histogram of Gradient”. 21 International Conference on Pattern Recognition (ICPR 2012).

[15]. James Sahaya Mary, (2015). “A Review of Daugman's Algorithm in Iris Segmentation”. International Journal of Innovative Science, Engineering & Technology, Vol. 2, No. 8.

[16]. Kayaoglu M, and Uludag U, (2015). “Biometric Matching and Fusion System for Fingerprints from Non- Distal Phalanges”.

[17]. Kadry S, (2007). “A design and implementation of a wireless iris recognition attendance management system”. Information Technology and Control, Vol.36, No. 3.

[18]. Kaur Navjot, (2014). “An Iris Localization Method for Non-ideal Eye Images”. Journal of Emerging Technologies in Web Intelligence, Vol. 6, No. 4.

[19]. Krishna M, and Reddy S, (2014). “Automated Iris Localization using Active Contour Model for IRIS Recognition”. International Journal of Ethics in Engineering & Management Education, Vol. 1, No. 2.

[20]. More Manisha, Narale Vishakha and Tonge Vanita, (2015). “A Survey on Iris Recognition Techniques”. International Journal of Novel Research in Computer Science and Software Engineering, Vol. 2, No. 1.

[21]. Nguyen Thanh, Kien and Fookes, Clinton B. and Sridharan Sridha, (2010). “Fusing shrinking and expanding th active contour models for robust IRIS segmentation”. 10 International Conference on Information Science, Signal Processing and their Applications, 10-13 May 2010, Renaissance Hotel, Kuala Lumpur.

[22]. Nithyanandam S, Gayathri K. S, and Priyadarshini P. L. K, (2011). “A New Iris Normalization Process For Recognition System With Cryptographic Techniques”. IJCSI International Journal of Computer Science Issues, Vol. 8, No. 4, No 1.

[23]. Nkole Ifeanyi Ugbaga, Sulong Ghazali Bin, and Saparudin (n.d). “An Enhanced Iris Segmentation Algorithm Using Circle Hough Transform”. International Conference, Kudai, Johor, Malaysia.

[24]. Rizon M, Yazid H, Saad P, and Saad A. R, (2005). “Object Detection using Circular Hough Transform”. American Journal of Applied Sciences, Vol. 2, No. 12, pp. 1606-1609.

[25]. Ross Arun and Shah Samir, (2009). “Iris Segmentation using Geodesic Active Contours”. IEEE Transactions, Vol. 4, No. 4.

[26]. Soltany Milad, Zadeh Saeid Toosi and Pourreza Hamid-Reza, (2012). “Daugman's Algorithm Enhancement for Iris Localization”. Advanced Materials Research, pp. 403-408

[27]. Suen Ching Y, Bhattacharya Prabir, and Roy Kaushik, (2010). “Unideal Iris Segmentation using Region-Based Active Contour Mode”. Springer-Verlag, Berlin, Heidelberg 2010.

[28]. Sundaresan M, and S. Jayalakshmi, (2014). “A Study of Iris Segmentation Methods using Fuzzy C Means and KMeans Clustering Algorithm”. International Journal of Computer Applications, Vol. 85, No 11.

[29]. T. K Sruthi, and K. M Jini, (2013). “A Literature Review on Iris Segmentation Techniques for Iris Recognition Systems”. Vol. 11, No. 1.

[30]. Walia Mrigana and Jain Shaily, (2015). “Iris Recognition System Using Circular Hough Transform”. International Journal of Advance Research in Computer Science and Management Studies, Vol. 3, No. 7.

[31]. Zhang Zhengben and Wang Chongke, (2015). “Research on Iris Localization Algorithm based on the Active Contour Model”. International Journal of Security and Its Applications, Vol. 9, No. 3.

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Research Paper

Feature Segmentation of Blood Vessels using Active Contour with Hybrid Region Information with Application to Retinal Images

Suri Sahithi* , G. Guru Prasad**

* PG Scholar, Department of Electronics and Communication Engineering, Sree Vidyanikethan Engineering College, Tirupathi, India.

** Assistant Professor, Department of Electronics and Communication Engineering, Sree Vidyanikethan Engineering College, Tirupathi, India.

Sahithi, S., and Prasad, G.G. (2016). Feature Segmentation of Blood Vessels using Active Contour with Hybrid Region Information with Application to Retinal Images. i-manager’s Journal on Image Processing, 3(1), 37-43.

Abstract

Computerized detection of blood vessel structures is becoming one of the most interesting parts in the field of diagnosis of the vascular diseases. The objective of this paper is to introduce a novel filter, based on a new kernel function with Cauchy distribution to improve the accuracy of the automated retinal vessel detection. Moreover, for a good segmentation performance, the proposed model has the benefit of using distinct types of region information. The aim of the proposed model is to increase the accuracy of an image.

References

[1]. Yitian Zhao, Lavdie Rada, Ke Chen, Simon P. Harding, and Yalin Zheng, (2015). “Automated Vessel Segmentation Using Infinite Perimeter Active Contour Model with Hybrid Region Information with Application to Retinal Images”. IEEE Trans On Medical Imaging, Vol. 34, No. 9, pp. 1797- 1807.

[2]. A. F. Frangi, W. J. Niessen, K. L. Vincken, and M. V. Viergever, (1998). “Multiscale vessel enhancement filtering”. In Med. Image Comput. Comput. Assist. Interv., Vol. 1496, pp. 130-137.

[3]. P. Bankhead, J. McGeown, and T. Curtis, (2009). “Fast retinal vessel detection and measurement using wavelets and edge location refinement”. Plos One, Vol. 7, No. 3.

[4]. G. Lathen, J. Jonasson, and M. Borga, (2010). “Blood vessel segmentation using multi-scale quadrature filtering”. Pattern Recogn. Lett., Vol. 31, pp. 762-767.

[5]. F. Rossant, M. Badellino, A. Chavillon, I. Bloch, and M. Paques, (2011). “A morphological approach for vessel segmentation in eye fundus images, with quantitative evaluation”. J. Med. Imaging. Health. Inf., Vol. 1, pp. 42-49.

[6]. Hooshiar Zolfagharnasab, Ahmad Reza Naghs-Nilchi, (2014). “Cauchy Based Matched Filter for Retinal Vessels Detection”. Journal of Medical Signals and Sensors, Vol. 4.

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Volume 3 Issue 1 January - March 2016 [Open Access]

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*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.

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Multispectral Image Compression with High Resolution Improved SPIHT for Testing Various Input Images

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Analysis of Iris Segmentation using Circular Hough Transform and Daughman's Method

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* PG Scholar, Department of Electronics and Communication Engineering, CSIT, Durg, Chhattisgarh, India. ** Associate Professor, Department of Electronics and Communication Engineering, CSIT, Durg, Chhattisgarh, India.

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Feature Segmentation of Blood Vessels using Active Contour with Hybrid Region Information with Application to Retinal Images

Suri Sahithi* , G. Guru Prasad**

* PG Scholar, Department of Electronics and Communication Engineering, Sree Vidyanikethan Engineering College, Tirupathi, India. ** Assistant Professor, Department of Electronics and Communication Engineering, Sree Vidyanikethan Engineering College, Tirupathi, India.

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*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.

Gunjan Seth* , Sukhvir Kaur, Jagdeep Singh*

V. Bhagya Raju,* , K. Jaya Sankar, C. D. Naidu*, Srinivas Bachu****

* PG Scholar, Department of Electronics and Communication Engineering, CSIT, Durg, Chhattisgarh, India.

** Associate Professor, Department of Electronics and Communication Engineering, CSIT, Durg, Chhattisgarh, India.

* PG Scholar, Department of Electronics and Communication Engineering, Sree Vidyanikethan Engineering College, Tirupathi, India.

** Assistant Professor, Department of Electronics and Communication Engineering, Sree Vidyanikethan Engineering College, Tirupathi, India.