Top 10 Signal and Image Processing Papers

Signal & Image Processing: An International Journal (SIPIJ)

ISSN: 0976 – 710X [Online]; 2229 – 3922 [Print] 

http://www.airccse.org/journal/sipij/index.html

Citation Count – 177

Content Based Image Retrieval using Color and Texture 

Manimala Singha and K.Hemachandran  

Dept. of Computer Science, Assam University, Silchar India. Pin code 788011

Abstract

The increased need of content based image retrieval technique can be found in a number of different domains such as Data Mining, Education, Medical Imaging, Crime Prevention, Weather forecasting, Remote Sensing and Management of Earth Resources. This paper presents the content based image retrieval, using features like texture and color, called WBCHIR (Wavelet Based Color Histogram Image Retrieval).The texture and color features are extracted through wavelet transformation and color histogram and the combination of these features is robust to scaling and translation of objects in an image. The proposed system has demonstrated a promising and faster retrieval method on a WANG image database containing 1000 general-purpose color images. The performance has been evaluated by comparing with the existing systems in the literature.

Keywords

Image Retrieval, Color Histogram, Color Spaces, Quantization, Similarity Matching, Haar Wavelet, Precision and Recall.

For More Details  : http://aircconline.com/sipij/V3N1/3112sipij04.pdf

Volume Link : http://www.airccse.org/journal/sipij/vol3.html

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Citation Count – 84

ALGORITHM AND TECHNIQUE ON VARIOUS EDGE DETECTION: A SURVEY

Rashmi1, Mukesh Kumar2 and Rohini Saxena2

Department of Electronics and Communication Engineering,

SHIATS- Allahabad, UP.-India

Abstract

An edge may be defined as a set of connected pixels that forms a boundary between two disjoints regions. Edge detection is basically, a method of segmenting an image into regions of discontinuity. Edge detection plays an important role in digital image processing and practical aspects of our life. .In this paper we studied various edge detection techniques as Prewitt, Robert, Sobel, Marr Hildrith and Canny operators. On comparing them we can see that canny edge detector performs better than all other edge detectors on various aspects such as it is adaptive in nature, performs better for noisy image, gives sharp edges , low probability of detecting false edges etc.

Keywords

Edges, Edge detection, Canny edge detection.

For More Details  : http://aircconline.com/sipij/V4N3/4313sipij06.pdf

Volume Link : http://www.airccse.org/journal/sipij/vol4.html

References

[1]   James Clerk Maxwell,1868 DIGITAL IMAGE PROCESSING Mathematical and Computational Methods.

[2]   R .Gonzalez and R. Woods, Digital Image Processing, ,Addison Wesley, 1992, pp 414 – 428.

[3]   S. Sridhar, Oxford university publication. , Digital Image Processing.

[4]   Shamik Tiwari , Danpat Rai & co.(P) LTD. “Digital Image processing”

[5]   J. F. Canny. “A computational approach to edge detection”. IEEE Trans. Pattern Anal. Machine Intell., vol.PAMI-8, no. 6, pp. 679-697, 1986 Journal of Image Processing (IJIP), Volume (3) : Issue (1)

[6]   Geng Xing, Chen ken , Hu Xiaoguang “An improved Canny edge detection algorithm for color image” IEEE TRANSATION ,2012 978-1-4673-0311-8/12/$31.00 ©2012 IEEE.

[7]   Yuesong Mei, Jianqiao Yu “ An Algorithm for Automatic Extraction of Moving Object in the Image Guidance”, IEEE, International Conference on Intelligent System Design and Engineering Application,2010.978-0-7695-4212-6/10 $26.00 © 2010 IEEE DOI 10.1109/ISDEA.2010.253

[8]   Xiaogbin Wang, Baokui Li, Qingbo Geng , “Runway Detection and Tracking for Unmanned Aerial Vehicle Based on an Improved Canny Edge Detection Algorithm”IEEE, 4th International Conference on Intelligent Human-Machine Systems and Cybernetics, 2012. 978-0-7695-4721-3/12 $26.00 © 2012 IEEE DOI 10.1109/IHMSC.2012.132

[9]   Sos Agaian, Ali Almuntashri “Noise-Resilient Edge Detection Algorithm for Brain MRI Images”, IEEE , 31st Annual International Conference of the IEEE EMBS Minneapolis, Minnesota, USA, September 2-6, 2009.978-1-4244-3296-7/09/$25.00 ©2009 IEEE.

[10] Fan Chun-ling, Wang Dao-he “The Application of Adaptive Canny Algorithm in the Cable Insulation Layer Measurement” IEEE, Second International Workshop on Computer Science and Engineering, 978-0-7695-3881-5/09 $26.00 © 2009 IEEE DOI 10.1109/WCSE.2009.177

[11] PENG Zhao-yi , ZHU Yan-hui , ZHOU Yu “Real-time Facial Expression Recognition Based on Adaptive Canny Operator Edge Detection”. IEEE, Second International Conference on Multimedia and Information Technology, 2010. 978-0-7695-4008-5/10 $26.00 © 2010 IEEE DOI 10.1109/MMIT.2010.100 154

[12] Jianjum Zhao, Heng Yu, Xiaoguang Gu and Sheng Wang. “The Edge Detection of River model Based on Self-adaptive Canny Algorithm And Connected Domain Segmentation” IEEE,Proceedings of the 8th World Congress on Intelligent Control and Automation July 6-9 2010, Jinan, China, 978-1- 4244-6712-9/10/$26.00 ©2010 IEEE

[13] Mee-Li Chiang, Siong-Hoe Lau “Automatic Multiple Faces Tracking and Detection using Improved Edge Detector Algorithm” IEEE 7th International Conference on IT in Asia (CITA),2011 ,978-1- 61284-130-4/11/

[14] Lejiang Guo,Yahui Hu Ze Hu, Xuanlai Tang “The Edge Detection Operators and Their Application in License Plate Recognition, IEEE TRANSATION 2010, 20978-1-4244-5392-4/10/.

 

Citation Count – 71

TWO NEW APPROACHES FOR SECURED IMAGE STEGANOGRAPHY USING CRYPTOGRAPHIC TECHNIQUES AND TYPE CONVERSIONS

Sujay Narayana1 and Gaurav Prasad2

1Department of Electronics and Communication, NITK, Surathkal, India

2Department of Information Technology, NITK, Surathkal, India

Abstract

The science of securing a data by encryption is Cryptography whereas the method of hiding secret messages in other messages is Steganography, so that the secret’s very existence is concealed. The term ‘Steganography’ describes the method of hiding cognitive content in another medium to avoid detection by the intruders. This paper introduces two new methods wherein cryptography and steganography are combined to encrypt the data as well as to hide the encrypted data in another medium so the fact that a message being sent is concealed. One of the methods shows how to secure the image by converting it into cipher text by S-DES algorithm using a secret key and conceal this text in another image by steganographic method. Another method shows a new way of hiding an image in another image by encrypting the image directly by S-DES algorithm using a key image and the data obtained is concealed in another image. The proposed method prevents the possibilities of steganalysis also.

Keywords

Steganography, Cryptography, image hiding, least-significant bit (LSB) method.

For More Details  : http://aircconline.com/sipij/V1N2/1210sipij06.pdf

Volume Link : http://www.airccse.org/journal/sipij/vol1.html

References

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Citation Count – 64

A REVIEW PAPER: NOISE MODELS IN DIGITAL IMAGE PROCESSING

Ajay Kumar Boyat1 and Brijendra Kumar Joshi2

1Research Scholar, Department of Electronics Telecomm and Computer Engineering, Military College of Tele Communication Engineering, Military Head Quartar of War (MHOW), Ministry of Defence, Govt. of India, India

2Professor, Department of Electronics Telecomm and Computer Engineering, Military College of Tele Communication Engineering, Military Head Quartar of War (MHOW), Ministry of Defence, Govt. of India, India  

Abstract

Noise is always presents in digital images during image acquisition, coding, transmission, and processing steps. Noise is very difficult to remove it from the digital images without the prior knowledge of noise model. That is why, review of noise models are essential in the study of image denoising techniques. In this paper, we express a brief overview of various noise models. These noise models can be selected by analysis of their origin. In this way, we present a complete and quantitative analysis of noise models available in digital images.

Keywords

Noise model, Probability density function, Power spectral density (PDF), Digital images.

For More Details  : http://aircconline.com/sipij/V6N2/6215sipij06.pdf

Volume Link : http://www.airccse.org/journal/sipij/vol6.html

References

[1]   Gonzalez R. C., & Woods R. E. (2002) “Digital Image Processing,” second ed., Prentice Hall, Englewood, Cliffs, NJ.

[2]   Bovick A. (2000) “Handbook of Image and Video processing,” Acedemic press, New York.

[3]   Patil, J. & Jadhav S. (2013) “A Comparative Study of Image Denoising Techniques,” International Journal of Innovative Research in Science, Engineering and Technology, Vol. 2, No. 3.

[4]   Dougherty G. (2010) “Digital Image Processing for Medical Applications,” second ed., Cambridge university press.

[5]   Boyat, A. and Joshi, B. K. (2013) “Image Denoising using Wavelet Transform and Median Filtering’, IEEE Nirma University International Conference on Engineering,” Ahemdabad.

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[9]   Bhattacharya J. K., Chakraborty D., & Samanta H. S., (2005) “Brownian Motion – Past and Present,” Cornall university library. arXiv:cond-mat/0511389

[10] Radenovic A., “Brownian motion and single particle tracking,” Advanced Bioengineering methods laboratory, Ecole polyteachenique federal de Lausanne.

[11] Peidle J., Stokes C., Hart R., Franklin M., Newburgh R., Pahk J., Rueckner W. & Samuel AD, (2009) “Inexpensive microscopy for introductory laboratory courses,” American Journal of Physics Vol. 77 pp. 931-938.

[12] Nakroshis P., Amoroso M., Legere J. & Smith C., (2003) “Measuring Boltzmann’s constant using video microscopy of Brownian motion,” American Journal of Physics, Vol. 71, No. 6, pp. 568-573.

[13] Chabay R. W., & Sherwood B. A., (2009) “Matter and Interactions,” 3rd edition, John Wiley and Sons.

[14] Joshi, A., Boyat, A. and Joshi, B. K. (2014) “Impact of Wavelet Transform and Median Filtering on removal of Salt and Pepper noise in Digital Images,” IEEE International Conference on Issues and Challenges in Intelligant Computing Teachniques, Gaziabad.

[15] Hosseini H. & Marvasti F., (2013) “Fast restoration of natural images corrupted by high-density impulse noise,” EURASIP Journal on Image and Video Processing. doi:10.1186/1687-5281-2013-15

[16] Koli M. & Balaji S., (2013) “Literature survey on impulse noise reduction,” Signal & Image Processing : An International Journal (SIPIJ) Vol.4, No.5.

[17] Benzarti F. & Amiri H., (2013) “Speckle Noise Reduction in Medical Ultrasound Images,” Signal, Image and Pattern Recognition Laboratory, Engineering School of Tunis (ENIT).

[18] Kaur T., Sandhu M. & Goel P. “Performance Comparison of Transform Domain for Speckle Reduction in Ultrasound Image” International Journal of Engineering Research and Application, Vol. 2, Issue 1, pp.184-188.

[19] Salivahanan S., Vallavaraj A. & Gnanapriya C. (2008) “Digital Signal Processing,” Tata McgrawHill, Vol. 23, NewDelhi.

[20] Zhang L., Dong W., Zhang D. & Shi G. (2010) “Two stage denoising by principal component analysis with local pixel grouping,” Elsevier Pattern Recognition, Vol. 43, Issue 4, pp. 1531-1549.

[21] Boyat, A. and Joshi, B. K. (2014) ‘Image Denoising using Wavelet Transform and Wiener Filter based on Log Energy Distribution over Poisson-Gaussian Noise Model’, In Press, IEEE International Conference on Computational Intelligence and Computing Research, Coimbatore.

[22] Luisier, F., Blu, T. and Unser, M. (2011) ‘Image denoising in mixed Poisson-Gaussian noise’, IEEE Trans. Image Process., Vol. 20, No. 3, pp. 696–708.

[23] Makitalo, M. and Foi, A. (2013) “Optimal inversion of the genralized Anscombe transformation for Poisson-Gaussian noise,” IEEE Trans. Image Process., vol. 22, no. 1, pp. 91-103.

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[25] Schowengerdt R. A. (1983) “Techniques for Image Processing and classifications in Remote Sensing,” First Edition Academic Press.

[26] Kamboj P. & Rani V., (2013) “A Brief study of various noise models and filtering techniques,” Journal of Global Research in Computer Science, Vol. 4, No. 4.

[27] T. Chhabra, G. Dua and T. Malhotra (2013) “Comparative Analysis of Denoising Methods in CT Images” International Journal of Emerging Trends in Electrical and Electronics, Vol. 3, Issue 2.

 

Citation Count – 64

RED BLOOD CELLS ESTIMATION USING HOUGH TRANSFORM TECHNIQUE

Nasrul Humaimi Mahmood and Muhammad Asraf Mansor

Department of Biomedical Instrumentation and Signal Processing,

Faculty of Health Science and Biomedical Engineering,

Universiti Teknologi Malaysia, Johor, Malaysia

Abstract

The number of red blood cells contributes more to clinical diagnosis with respect to blood diseases. The aim of this research is to produce a computer vision system that can detect and estimate the number of red blood cells in the blood sample image. Morphological is a very powerful tool in image processing, and it is been used to segment and extract the red blood cells from the background and other cells. The algorithm used features such as shape of red blood cells for counting process, and Hough transform is introduced in this process. The result presented here is based on images with normal blood cells. The tested data consists of 10 samples and produced the accurate estimation rate closest to 96% from manual counting.

Keywords

Red blood cells, MATLAB, Hough Transform, Morphological Image Processing.

For More Details  : http://aircconline.com/sipij/V3N2/3212sipij04.pdf

Volume Link : http://www.airccse.org/journal/sipij/vol3.html

References

[1]   Michael R. Pinsky, Laurent Brochard and Jordi Mancebo. “Applied Physiology in Intensive Care Medicine”. Springer. 229-238, 2007.

[2]   Dondorp AM, Angus BJ, Chotivanich K, Silamut K, Ruangveerayuth R, Hardeman MR, Kager PA, Vreeken J,White NJ. “Red cell deformability as a predictor of anemia in severe falciparum malaria”. Am J Trop Med Hyg 60: 733–744; 1999.

[3]   Medicine Health – http://www.medicinehealth.com/leukemia/article.html

[4]        Yuzhang WEI. “The Research of Urinary Sediment Visual Component Analysis Based on Fuzzy Clustering”. Nanjing Information Engineering University, 2008:3-4,15-32.

[5]   Ran Ding. “Algorithm Research on Recognition and Classification of Microscopic Urinary Sediment Images”. Jilin University, 2006: 9-16, 20-31.

[6]   C.D. Ruberto, A.G. Dempster, S. Khan and B. Jarra. “Segmentation of Blood Image using Morphological Operators”. Proceeding 15th International Conference on Pattern Recognition. vol. 3, pp. 397-400, 2000.

[7]   Tahir Rabbani and Frank van den Heuvel, “Efficient hough transform for automatic detection of cylinders in point clouds” in Proceedings of the 11th Annual Conference of the Advanced School for Computing and Imaging (ASCI ’05), The Netherlands, June 2005.

[8]   Roy A. Dimayuga, Gerwin T. Ong, Rainier Carlo S. Perez, Gefferson O. Siy, Saman C. Sohrabi Langroudi and Miguel O.Gutierrez. “Leukemia Detection Using Digital Image Processing in Matlab”. ECE Student Forum, De La Salle University, Manila. March 26, 2010.

[9]   Ramin Soltanzadeh. “Classification of Three Types of Red Blood Cells in Peripheral Blood Smear Based on Morphology. Proceedings of ICSP, 2010.

[10] Heidi Berge, Dale Taylor, Sriram Krishnan, and Tania S. Douglas. Improved Red Blood Cell Counting in thin Blood Smears. Proceedings of ISBI, 2011. pp.204-207.

[11] Zack G.W., Rogers W.E. and Latt S.A. “Automatic-measurement of sister chromatid exchange frequency.” Journal of Histochemistry & Cytochemistry 25, 1977, 741-753.

[12] Guitao Cao, Cai Zhong,Ling Li and Jun Dong. “Detection of Red Blood Cell in Urine Micrograph”. The 3rd International Conference on Bioinformatics and Biomedical Engineering (ICBBE). 2009.

[13] Centers for Disease Control and Preventation Public Health Image Library (online) – http://phil.cdc.gov/phil/home.asp

[14] Blood cell histology – http://www.unomaha.edu/hpa/blood.html

[15] University of Utah Libarary – http://library.med.utah.edu/WebPath/HEMEHTML/HEME005.html

[16] Kenneth. R. Castleman, Z. G. Zhu. “Digital Image Processing”. Publishing House of Electronics Industry, Beijing, 1999.

[17] W. Meisel. “Computer-Oriented Approaches to Pattern Recognition”. Academic Press. New York, 1972.

[18] Shapiro, Linda and Stockman, George. “Computer Vision”. Prentice-Hall, 2001.

 

Citation Count – 64

EFFICIENT CBIR USING COLOR HISTOGRAM PROCESSING

Neetu Sharma. S1, Paresh Rawat S2 and Jaikaran Singh.S3

1,2Department of Electronics and Communication, Truba Institute of Engineering and Information Technology,Bhopal

3Department of Electronics and Communication, Sri Satya Sai Institute of Science and Technology, Sehore

Abstract

The need for efficient content-based image retrieval system has increased hugely. Efficient and effective retrieval techniques of images are desired because of the explosive growth of digital images. content based image retrieval (CBIR) is a promising approach because of its automatic indexing retrieval based on their semantic features and visual appearance. The similarity of images depends on the feature representation. However users have difficulties in representing their information needs in queries to content based image retrieval systems. In this paper we investigate two methods for describing the contents of images. The first one characterizes images by global descriptor attributes, while the second is based on color histogram approach.To compute feature vectors for Global descriptor, required time is much less as compared to color histogram. Hence cross correlation value & image descriptor attributes are calculated prior histogram implementation to make CBIR system more efficient.The performance of this approach is measured and results are shown. The aim of this paper is to compare various global descriptor attributes and to make CBIR system more efficient. It is found that further modifications are needed to produce better performance in searching images.

Keywords

CBIR, Image Retrieval, Feature extraction, Global descriptor.

For More Details  : http://aircconline.com/sipij/V2N1/2111sipij08.pdf

Volume Link : http://www.airccse.org/journal/sipij/vol2.html

References

[1]   Shengjiu Wang, “A Robust CBIR Approach Using Local Color Histograms,” Technical Report TR 01-03, Departement of computing science, University of Alberta, Canada. October 2001.

[2]   R. Schettini, G. Ciocca, S Zuffi. A survey of methods for colour image indexing and retrieval in image databases. Color Imaging Science: Exploiting Digital Media, (R. Luo, L. MacDonald eds.), J. Wiley, 2001.

[3]   R. Russel, P Sinha. Perceptually based Comparison of Image Similarity Metrics.,MIT AI Memo 2001-014. Massachusetts Institute of Technology, 2001

[4]   J.F. Omhover, M. Detyniecki and B. Bouchon-Meunier, “A Region Similarity Based Image Retrieval System”, The 10th International conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems Perugia,Italy 2004.

[5]   OleAndreasFlaatonJonsgard, Improvements on color histogram based CBIR,2005.

[6]   Ryszard S. Chora´s”Image Feature Extraction Techniques and Their Applications for CBIR and Biometrics Systems” international journal of biology and biomedical engineering,2007.

[7]   H. B. Kekre , Dhirendra Mishra “CBIR using Upper Six FFT Sectors of Color Images for Feature Vector Generation” H.B.Kekre. et al /International Journal of Engineering and Technology Vol.2(2), 2010, 49-54.

[8]   Ch.Srinivasa rao , S. Srinivas kumar #, B.N.Chatterji “ Content Based Image Retrieval using Contourlet Transform” ICGST-GVIP Journal, Volume 7, Issue 3, November 2007.

[9]   Dr. H. B. Kekre Kavita Sonavane “CBIR Using Kekre’s Transform over Row column Mean and Variance Vector ” (IJCSE) International Journal on Computer Science and Engineering Vol. 02, No. 05, 2010, 1609-1614.

[10] S. Nandagopalan, Dr. B. S. Adiga, and N. Deepak “A Universal Model for Content-Based Image Retrieval” World Academy of Science, Engineering and Technology 46 2008.

[11] P. B. Thawari & N. J. Janwe “CBIR Based On Color And Texture” International Journal of Information Technology and Knowledge Management January-June 2011, Volume 4, No. 1, pp. 129-132.

[12] Jalil Abbas, Salman Qadri, Muhammad Idrees3, Sarfraz Awan, Naeem Akhtar Khan1 “Frame Work For Content Based Image Retrieval (Textual Based) System” Journal of American Science 2010;6(9).

[13] Ramesh Babu Durai C “A Generic Approach To Content Based Image Retrieval Using Dct And Classification Techniques” (IJCSE) International Journal on Computer Science and Engineering Vol. 02, No. 06, 2010, 2022-2024.

[14] Ch.Srinivasa Rao , S.Srinivas Kumar and B.Chandra Mohan “ CBIR Using Exact Legendre Moments And Support Vector Machine” International Journal Of Multimedia And Its Applications Vol.2, No.2, May 2010.

[15] Hichem Bannour_Lobna Hlaoua_Bechir Ayeb, “Survey Of The Adequate Descriptor For Content Based Image Retrieval On The Web:Global Versus Local Features “ 2009.

[16] Hiremath P.S. and Jagadeesh Pujari “Content Based Image Retrieval using Color Boosted Salient Points and Shape features of an image” International Journal of Image Processing, Volume (2) : Issue (1).

[17] Zhe-Ming Lu, Su-Zhi Li and Hans Burkhardt , “ A Content-Based Image Retrieval Scheme In JPEG Compressed Domain ” International Journal of Innovative Computing, Information and Control ICIC International °c 2006 ISSN 1349-4198 Volume 2, Number 4, August 2006.

[18] Issam El-Naqa, Yongyi Yang , Nikolas P. Galatsanos , Robert M. Nishikawa , and Miles N. Wernick , “A Similarity Learning Approach to Content-Based Image Retrieval: Application to Digital Mammography ” Ieee Transactions On Medical Imaging, Vol. 23, No. 10, October 2004 1233.

[19] Joel Ponianto , “Content-Based Image Retrieval Indexing” School of Computer Science and Software Engineering Monash University, 2005.

[20] Sameer Antani, L. Rodney Long, George R. Thoma , “Content-Based Image Retrieval for Large Biomedical Image Archives ” MEDINFO 2004 M. Fieschi et al. (Eds) Amsterdam: IOS Press © 2004 IMIA.

[21] Stefan Uhlmann, Serkan Kiranyaz and Moncef Gabbouj, “A Regionalized Content-Based Image Retrieval Framework” 15th European Signal Processing Conference (EUSIPCO 2007), Poznan, Poland, September 3-7, 2007, copyright by EURASIP.

[22] Ricardo da S. Torres, Alexandre X. Falco , “A New Framework to Combine Descriptors for Content based Image Retrieval ” IKM’05, October 31.November 5, 2005, Bremen, Germany. Zhong Su, Hongjiang Zhang, Stan Li, and Shaoping Ma , “Relevance Feedback in Content-Based Image Retrieval: Bayesian Framework, Feature Subspaces, and Progressive Learning” IEEE Transactions On Image Processing, Vol.12, No. 8, August 2003.

 

Citation Count – 52

HISTOPATHOLOGICAL IMAGE ANALYSIS USING IMAGE PROCESSING TECHNIQUES: AN OVERVIEW

D. Belsare and M. M. Mushrif

Department of Electronics & Telecommunication Engineering, Yeshwantrao Chavan College of Engineering, Hingna Road, Nagpur, MH, India

Abstract

This paper reviews computer assisted histopathology image analysis for cancer detection and classification. Histopathology refers to the examination of invasive or less invasive biopsy sample by a pathologist under microscope for locating, analyzing and classifying most of the diseases like cancer. The analysis of histoapthological image is done manually by the pathologist to detect disease which leads to subjective diagnosis of sample and varies with level of expertise of examiner. The pathologist examine the tissue structure, distribution of cells in tissue, regularities of cell shapes and determine benign and malignancy in image. This is very time consuming and more prone to intra and inter observer variability. To overcome this difficulty a computer assisted image analysis is needed for quantitative diagnosis of tissue. In this paper we reviews and summarize the applications of digital image processing techniques for histology image analysis mainly to cover segmentation and disease classification methods.

Keywords

Image processing, histopathological image analysis, image segmentation, and computer assisted diagnosis.

For More Details  : http://aircconline.com/sipij/V3N4/3412sipij03.pdf 

Volume Link : http://www.airccse.org/journal/sipij/vol3.html

References

[1]   Cigdem Demir And B’Ulent Yener, “Automated Cancer Diagnosis Based On Histopathological Images: A Systematic Survey”, Technical Report, Rensselaer Polytechnic Institute, Department Of Computer Science, Tr-05-09. 1

[2]   S. Waheed, R. A. Moffitt, Q. Chaudryl, A. N. Young, and M.D. Wang “Computer Aided Histopathological Classification of Cancer Subtypes”, 1-4244-1509-8/07,2007 IEEE

[3]   Olcay Sertel, Umit V. Catalyurek, Hiroyuki Shimada, and Metin N. Gurcan, “ Computer-aided Prognosis of Neuroblastoma: Detection of Mitosis and Karyorrhexis Cells in Digitized Histological Images”,31st Annual International Conference of the IEEE EMBS Minneapolis, Minnesota, USA, September 2-6, 2009

[4]   Ajay Basavanhallya, Elaine Yu, Jun Xu, Shridar Ganesan, Michael Feldman, John Tomaszewski, Anant Madabhushi, “Incorporating Domain Knowledge for Tubule Detection in Breast Histopathology Using O’Callaghan Neighborhoods”, Medical Imaging 2011: Computer-Aided Diagnosis, Proc. of SPIE Vol. 7963, 796310, doi: 10.1117/12.878092

[5]   Jun Xu, Andrew Janowczyk, Sharat Chandran, Anant Madabhushi, “A Weighted Mean Shift, Normalized Cuts Initialized Color Gradient Based Geodesic Active Contour Model: Applications to Histopathology Image Segmentation”, Medical Imaging 2010: Image Processing, Proc. of SPIE Vol. 7623, 76230Y, doi: 10.1117/12.845602

[6]   H. Fatakdawala, J. Xu, A. Basavanhally, G. Bhanot, S. Ganesan, M. Feldman, J. E. Tomaszewski, and A. Madabhushi, “Expectation maximization driven geodesic active contour with overlap resolution (emagacor): application to lymphocyte segmentation on breast cancer histopathology,” Biomedical Engineering, IEEE Transactions on , In Press. 2009 Ninth IEEE International Conference on Bioinformatics and Bioengineering

[7]  Jean-Romain Dalle, Wee Kheng Leow, Daniel Racoceanu, Adina Eunice Tutac, Thomas C. Putti, “Automatic Breast Cancer Grading of Histopathological Images”, 30th Annual International IEEE EMBS Conference Vancouver, British Columbia, Canada, August 20-24, 2008

[8]   Baochuan Pang, Yi Zhang, Qianqing Chen, Zhifan Gao, Qinmu Peng, Xinge You,”Cell Nucleus Segmentation in Color Histopathological Imagery Using Convolutional Networks”, 978-1-4244-7210-9/10, 2010 IEEE

[9]   S. Naik, S. Doyle, S. Agner, A. Madabhushi, M. Feldman, and J. Tomaszewski, “Automated gland and nuclei segmentation for grading of prostate and breast cancer histopathology,” in Biomedical Imaging: From Nano to Macro, 2008. ISBI 2008. 5th IEEE International Symposium on, pp. 284–287, May 2008.

[10] Scott Doyle, Shannon Agner, Anant Madabhushi, Michael Feldman, John Tomaszewski, “Automated Grading Of Breast Cancer Histopathology Using Spectral Clustering with Textural and Architectural Image Features”, 978-1-4244-2003-2/08, 2008 IEEE.

[11] Metin N. Gurcan, Tony Pan, Hiro Shimada, and Joel Saltz, “Image Analysis for Neuroblastoma Classification: Segmentation of Cell Nuclei”, Proceedings of the 28th IEEE EMBS Annual International Conference New York City, USA, Aug 30-Sept 3, 2006

[12] Xiaobo Zhou, Fuhai Li, Jun Yan, and Stephen T. C. Wong “A Novel Cell Segmentation Method and Cell Phase Identification Using Markov Model”, IEEE Transactions On Information Technology In Biomedicine, Vol. 13, No. 2, March 2009

[13] Cigdem Gunduz-Demir, Melih Kandemir, Akif Burak Tosun, Cenk Sokmensuer, “Automatic segmentation of colon glands using object-graphs”, Medical Image Analysis 14(2010) 1-12

[14] Yousef Al-Kofahi, Wiem Lassoued, William Lee, and Badrinath Roysam “Improved Automatic Detection and Segmentation of Cell Nuclei in histopathology Images,” IEEE Transactions On Biomedical Engineering, Vol. 57, No. 4, April 2010 Pp 841-850

[15] Maciej Hrebień, Jozef Korbicz, and Andrzej Obuchowicz” Hough Transform, Search Strategy and Watershed Algorithm in segmentation of Cytological Images”:Computer Recognition Systems 2, ASC 45, pp. 550–557,Springer-Verlag Berlin Heidelberg, 2007

[16] Omar S. Al-Kadi, “Texture measures combination for improved meningioma classification of histopathological images”, Pattern Recognition 43(2010) 2043-2053

[17] T.S. Subashini, V. Ramalingam, S. Palanivel, “Breast mass classification based on cytological patterns using RBFNN and SVM”, Expert Systems with Applications,2008

[18] Olcay Sertel • Jun Kong • Umit V. Catalyurek • Gerard Lozanski • Joel H. Saltz • Metin N. Gurcan , J Sign Process Syst , “Histopathological Image Analysis Using Model-Based Intermediate Representations and Color Texture: Follicular Lymphoma Grading”, DOI 10.1007/s11265-008-0201

[19] P. S. Umesh Adiga, B. B. Chaudhari, “Region based techniques for segmentation of volumetric histopathological images”, Computer Methods & Programs in Biomedicine 61(2000) 23-47

[20] Ajay Nagesh Basavanhally, Shridar Ganesan, Shannon Agner, James Peter Monaco, Michael D.Feldman, John E. Tomaszewski, Gyan Bhanot, and Anant Madabhushi, “Computerized Image Based Detection and Grading of Lymphocytic Infiltration in HER2+ Breast Cancer Histopathology”, IEEE Transactions On Biomedical Engineering, Vol. 57, No. 3, March 2010,Pp 642-53

[21] Pornchai Phukpattaranont and Pleumjit Boonyaphiphat, Non-members “Color Based Segmentation of Nuclear Stained Breast Cancer Cell Images”, ECTI Transactions On Electrical Eng., Electronics and Communications Vol.5, No.2 August 2007

[22] M. Muthu Rama Krishnan, Mousumi Pal, Suneel K Bomminayuni, Chandan Chakraborty, Ranjan Rashmi Paul, Jyotirmoy Chatterjee, Ajoy K Roy, “Automated classification of cells in sub-epithelial connective tissue of oral sub-mucous fibrosis – An SVM based approach”, Computers in Biology & Medicine 39(2009) 1096-1104

[23] Olcay Sertel, Umit V. Catalyurek, Gerard Lozanski, Arwa Shanaah, Metin N. Gurcan, “ An Image Analysis Approach for Detecting Malignant Cells in Digitized H&E-stained Histology Images of Follicular Lymphoma”, 1051-4651/10, 2010 IEEE, DOI 10.1109/ICPR.2010.76

[24] M Muthu Rama Krishnan, Pratik Shaht, Madhumala Ghosh, Mousumi Pal, Chandan Chakraborty, Ranjan R Paul, Jyotirmoy Chatterjee and Ajoy K. Ray, “Automated Characterization of Subepithelial Connective Tissue Cells of Normal Oral Mucosa: Bayesian Approach”, Proceedings of the 2010 IEEE Students’ Technology Symposium 3-4 April 2010, IIT Kharagpur

[25] Scott Doyle, Michael Feldman, John Tomaszewski, and Anant Madabhushi, “A Boosted Bayesian Multi-Resolution Classifier for Prostate Cancer Detection from Digitized Needle Biopsies”, Transactions On Biomedical Engineering

[26] M. Murat Dundar, Sunil Badve, Gokhan Bilgin, Vikas Raykar, Rohit Jain, Olcay Sertel, and Metin N. Gurcan, “Computerized Classification of Intraductal Breast Lesions Using Histopathological Images”, IEEE Transactions On Biomedical Engineering, Vol. 58, No. 7, July 2011, pp. 1977-84.

[27] Akif Burak Tosun, Cigdem Gunduz-demir, “Graph Run-length Matrices For Histopathological Image Segmentation”, IEEE Transactions On Medical Imaging, Vol. 30, No. 3, March 2011, pp. 721-31.

[28] Hui Kong, Metin Gurcan, and Kamel Belkacem-Boussaid, “Partitioning Histopathological Images: An Integrated Framework for Supervised Color-Texture Segmentation and Cell Splitting”, IEEE Transactions On Medical Imaging, Vol. 30, No. 9, September 2011, pp. 1661-77

[29] Frank Y. Shih, Shouxian Cheng, “Automatic seeded region growing for color image segmentation”, Image and Vision Computing 23 (2005) 877-886, doi:10.1016/j.imavis.2005.05.015

[30] Tie Qi Chen, Yi Lu, “Color image segmentation-an innovative approach”, Pattern Recognition 35 (2002) 395-405

[31] Dogan Aitunbay, Celal Cigir, Cenk Sokmensuer and Cigdem Gunduz-Demir, “Color Graphs for Automated Cancer Diagnosis and grading”, IEEE Transaction on Biomedical Engineering, Vol. 57, No. 3, March 2010.

[32] Akif Burak Tosun, Melih Kandemir, Cenk Sokmensure, Cigdem Gunduz-demir, “Object-oriented texture analysis for the unsupervised segmentation of biopsy images for cancer detection”, Pattern Recognition 42 (2009) 1104-1112,doi:10.1016/j.patcog.2008.07.007.

[33] R.C. Gonzalez, R.F. Woods, “Digital Image Processing”, 3rd ed. Pearson Prentice Hall, 2008.

[34] M. Gurcan, L. Boucheron, A.Can,A. Madabhushi, N. Rajpoot, B. Yener, “Histopathological image analysis: a review”, IEEE Reviews in Biomedical Engineering2(2009).

[35] C. G. Loukas, A. Linney, “A survey on histological image analysis-based assessment of three major biological factors influencing radiotherapy: proliferation, hypoxia and vasculature”, Computer Methods and Programs in Biomedicine 74(3) (2004) 183-199.

 

Citation Count – 50

SPEECH RECOGNITION USING HMM WITH MFCC- AN ANALYSIS USING FREQUENCY SPECRAL DECOMPOSION TECHNIQUE

Ibrahim Patel1 and Y. Srinivas Rao2

1Department of BME, Padmasri.

Dr.B.V.Raju Institute of Technology, Narsapur

2Department of Instrument Technology,

Andhra University, Vizag, A.P.

Abstract

This paper presents an approach to the recognition of speech signal using frequency spectral information with Mel frequency for the improvement of speech feature representation in a HMM based recognition approach. A frequency spectral information is incorporated to the conventional Mel spectrum base speech recognition approach. The Mel frequency approach exploits the frequency observation for speech signal in a given resolution which results in resolution feature overlapping resulting in recognition limit. Resolution decomposition with separating frequency is mapping approach for a HMM based speech recognition system. The Simulation results show an improvement in the quality metrics of speech recognition with respect to computational time, learning accuracy for a speech recognition system.

Keywords

Speech-recognition, Mel-frequencies, DCT, frequency decomposition, Mapping Approach, HMM, MFCC.

For More Details  : http://aircconline.com/sipij/V1N2/1210sipij09.pdf

Volume Link : http://www.airccse.org/journal/sipij/vol1.html

References

[1]   Varga A.P, and Moore R.K.: Hidden Markov Model decomposition of speech and noise, Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing, pp. 845-48, (1990)

[2]   Allen, J.B.: How do humans process and recognize speech IEEE Trans. on Speech and Audio Processing, vol. 2, no. 4, pp.567–577 (1994.)

[3]   Kim W. Kang, S. and Ko, H. : Spectral subtraction based on phonetic dependency and masking effects, IEEE Proc.- Vision, Image and Signal Processing, 147(5), pp 423–27 (2000)

[4]   R.J. Elliott, L. Aggoun and J.B. : Moore Hidden Markov Models: Estimation and Control”, Springer Verlag, (1995)

[5]   Fujimoto, M.; Riki, Y.A.: Robust speech recognition in additive and channel noise environments using GMM and EM algorithm. Acoustics, Speech, and Signal Processing, 2004. Proceedings. (ICASSP ’04). IEEE International Conference,Vol1 17–21 May (2004)

[6]   J .C.Segura, A.de la Torre, M.C.Benitez and A.M.Peinado: Model Based Compensation of the Additive Noise for Continuous Speech Recognition. Experiments Using AURORA II Database and Tasks, EuroSpeech’01,Vol.I, Page(s):I – 941–944

[7]   M.J.F.Gales and S.J.Young : Robust Continuous Speech Recognition Using Parallel Model Combination, IEEE Trans. Speech and Audio Processing, Vol.4, No.5, pp.352–359(1996).

[8]   Renals, S., Morgan, N., Bourlard, H., Cohen, M, and Franco, H. : Connectionist Probability Estimators in HMM Speech Recognition, IEEE Trans. on Speech and Audio Processing, vol. 2, no. 1, pp. 161–174, (1994.)

[9]   J. Neto, C. Martins and L. Almeida: Speaker-Adaptation in a Hybrid HMM-MLP Recognizer”, in Proceedings ICASSP ’96, Atlanta, Vol. 6, pp.3383–3386 (1996.)

[10] Sadaoki Furui : Digital speech processing , synthesis and recognition second edition

[11] Gajic, B.; Paliwal, Kuldip .K. “Robust speech recognition using features based on zero crossings with peak amplitudes” Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP ’03). 2003 IEEE International Conference Volume 1, 6-10

[12] Lockwood P. and Boudy J (1992), “Experiments with a non-linear Spectral Subtractor (NSS), HMM and the projection, for robust speech recognition in cars”, Speech Communication, Vol. 11, Nos. 2-3, pp.215-228.

[13] Das S., Bakis R., Nadas A., Nahamoo D., and Picheny M., (1 993), “Influence of background noise and microphone on the performance of the IBM TANGORA speech recognition system”, Proc. IEEE ICASSP , Australia, April 1994, VI, pp. 21-23.

[14] Gong Y., (1995), “Speech recognition in noisy environments: A survey”. Speech Communication, 16, pp.261-291

[15] Junqua J-C., Haton J-P., (1996), “Robustness in ASR: Fundamentals and Applications”, Kluwer Academic Publishers.

[16] Tamura S., (1 987), “An analysis of a noise reduction multi-layer neural network”,

[17] Tamura S., (1 990),“Improvements to the noise reduction neural network”, IEEE

[18] Xie F. and Campernolle D., (1994),“A family of ‘MLP’ based non-linear spectral estimators for noise reduction”, IEEE ICASSP ’94, pp. 53-56.

 

Citation Count – 49

 

IMAGE DENOISING USING NEW ADAPTIVE BASED MEDIAN FILTER

Suman Shrestha1,2

1University of Massachusetts Medical School, Worcester, MA 01655

2Department of Electrical and Computer Engineering, The University of Akron, Akron, OH 44325

Abstract

Noise is a major issue while transferring images through all kinds of electronic communication. One of the most common noise in electronic communication is an impulse noise which is caused by unstable voltage. In this paper, the comparison of known image denoising techniques is discussed and a new technique using the decision based approach has been used for the removal of impulse noise. All these methods can primarily preserve image details while suppressing impulsive noise. The principle of these techniques is at first introduced and then analysed with various simulation results using MATLAB. Most of the previously known techniques are applicable for the denoising of images corrupted with less noise density. Here a new decision based technique has been presented which shows better performances than those already being used. The comparisons are made based on visual appreciation and further quantitatively by Mean Square error (MSE) and Peak Signal to Noise Ratio (PSNR) of different filtered images.

Keywords

Impulse Noise, Nonlinear filter, Adaptive Filters, Decision Based Filters.

For More Details  : http://aircconline.com/sipij/V5N4/5414sipij01.pdf

Volume Link : http://www.airccse.org/journal/sipij/vol5.html

References

[1]   I. Pitas and A. N. Venetsanopoulos, “Nonlinear Digital Filters: Principles and Applications”, Boston, MA: Kluwer, 1990.

[2]   J. Astola and P. Kuosmanen, “Fundamentals of Nonlinear Digital Filtering”, CRC Press, 1997.

[3]   T. Sun, M. Gabbouj and Y. Neuvo, “Center weighted median filters: Some properties and their applications in image processing”, Signal Processing, vol. 35, Issue 3, pp 213-229, February 1994.

[4]   T. Chen, K. K. Ma, L.H. Chen, “Tri-State Median Filter for Image Denoising”, IEEE Transactions on Image Processing, vol. 8, Issue 12, pp 1834-1838, 1999.

[5]   Z. Wang, D. Zhang, “Progressive switching median filter for the removal of impulse noise from highly corrupted images”, IEEE Transactions on Circuits and Systems, vol. 46, Issue 1, pp 78-80, Jan 1999.

[6]   V. V. Khryashchev, A. L. Priorov; I. V. Apalkov, P. S. Zvonarev, “Image denoising using adaptive switching median filter”, IEEE International Conference on Image Processing , vol. 1, pp 117-120, 2005.

[7]   Y. Zhao, D. Li, Z. Li, “Performance enhancement and analysis of an adaptive median filter”, International Conference on Communications and Networking, pp. 651-653, 2007.

[8]   V. Backman, R. Gurjar, K. Badizadegan, I. Itzkan, R. R. Dasari, L.T. Perelman and M.S. Feld, “A New Fast and Efficient Decision-Based Algorithm for Removal of High-Density Impulse Noises”, Signal Processing Letters, IEEE , Vol. 14, Issue 3, pp 189-192, 2007

[9]   Z. Vasicek and L. Sekanina, “Novel Hardware Implementation of Adaptive Median Filters”, Design and Diagnostics of Electronic Circuits and Systems, IEEE, pp 1-6, April 2008.

[10] B. Caroline, G. Sheeba, J. Jeyarani, F. Salma Rosline Mary, “VLSI implementation and performance evaluation of adaptive filters for impulse noise removal”, Emerging Trends in Science, Engineering and Technology (INCOSET), 2012 International Conference on , vol., no., pp.294,299, 13-14 Dec. 2012.

[11] V.R. Vijaykumar, P.T. Vanathi, P. Kanagasabapathy and D. Ebenezer, “High Density Impulse Noise Removal Using Robust Estimation Based Filter”, IAENG International Journal of Computer Science, August 2008.

 

Citation Count – 44

REVIEW OF MOTION ESTIMATION AND VIDEO STABILIZATION TECHNIQUES FOR HAND HELD MOBILE VIDEO 

Paresh Rawat1 and Jyoti Singhai2

1Electronics & communication Deptt., TRUBA I.E.I.T Bhopal

2Electronics Deptt , MAINT Bhopal

Abstract

Video stabilization is a video processing technique to enhance the quality of input video by removing the undesired camera motions. There are various approaches used for stabilizing the captured videos. Most of the existing methods are either very complex or does not perform well for slow and smooth motion of hand held mobile videos. Hence it is desired to synthesis a new stabilized video sequence, by removing the undesired motion between the successive frames of the hand held mobile video. Various 2D and 3D motion models used for the motion estimation and stabilization. The paper presents the review of the various motion models, motion estimation methods and the smoothening techniques. Paper also describes the direct pixel based and feature based methods of estimating the inter frame error. Some of the results of the differential motion estimation are also presented. Finally it closes with a open discussion of research problems in the area of motion estimation and stabilization.

Keywords

Video Stabilization, Motion Models, Interframe Error, Motion Estimation.

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