146

References

[1] S. M. Larie and S.S. Abukmeil. Brain abnormality in

schizophrenia: a systematic and quantitative review of

volumetric magnetic resonance imaging studies. J. Psych.,

vol.172, pp.110–120, 1998.

[2] P. Taylor. Invited review: computer aids for decision-making

in diagnostic radiology - a literature review. Brit. J. Radiol..,

vol. 68, pp. 945–957, 1995.

[3] A.P. Zijdenbos and B.M. Dawant. Brain segmentation and

white matter lesion detection in MR images. Critical Reviews

in Biomedical Engineering, vol. 22, pp.401–465, 1994.

[4] A.J. Worth, N. Makris, V.S. Caviness, and D.N. Kennedy.

Neuroanatomical segmentation in MRI: technological

objectives. Int. J. Patt. Rec. Art. Intel. , vol.11, pp.1161–

1187, 1997.

[5] V.S. Khoo, D.P. Dearnaley, D.J. Finnigan, A. Padhani, S.F.

Tanner, and M.O. Leach. Magnetic resonance imaging (MRI):

considerations and applications in radiotheraphy treatment

planning. Radiother. Oncol.,vol. 42,pp.1–15, 1997.

[6] H.W. Muller-Gartner, J.M. Links, et al. Measurement of

radiotracer concentration in brain gray matter using positron

emission tomography: MRI-based correction for partial

volume effects. J. Cereb. Blood Flow Metab., vol. 12,pp.571–

583, 1992.

147

[7] N. Ayache, P. Cinquin, I. Cohen, L. Cohen, F. Leitner, and O.

Monga. Segmentation of complex three dimensional medical

objects: a challenge and a requirement for computer-assisted

surgery planning and performance. In R.H. Taylor, S.

Lavallee, G.C. Burdea, and R. Mosges, editors, Computer

integrated surgery: technology and clinical applications,

pp.59–74. MIT Press, 1996.

[8] W.E.L. Grimson, G.J. Ettinger, T. Kapur, M.E. Leventon, W.M.

Wells, et al. Utilizing segmented MRI data in image-guided

surgery. Int. J. Patt. Rec. Art. Intel., vol.11, pp.1367–1397,

1997.

[9] C. C. Boring, T. S. Squires, T. Tong, and S. Montgomery,

“Cancer statistics, 1994,” CA-A Cancer J. Clinicians, Vol.

44,No.1, pp. 7-26, Jan. 1994.

[10] S. Shapiro, W. Venet, P. Strax, L. Venet, and R. Roeser, “Ten-

to-fourteen-year effect of screening on breast cancer

mortality,” JNCZ, Vol. 69, pp. 349, 1982.

[11] R. G. Lester, “The contributions of radiology to the diagnosis,

management, and cure of breast cancer,” Radio. , Vol. 151,

pp. 1-7, April 1984.

[12] M. Moskowitz, “Benefit and risk,” in Breast Cancer Detection:

Mammography and Other Methods in Breast Imaging, 2nd

ed., L. W. Bassett and R. H. Gold, Eds. New York Grune and

Stratton, 1987.

148

[13] L. Tab& and P. B. Dean, “The control of breast cancer through

mammography screening: What is the evidence,” Radiol. Clin.

N. Amer.vol. 25, No.5, pp. 993-1005, Sept. 1987.

[14] Caseldine, R. Blamey, E. Roebuck, and C. Elston, Breast

Disease for Radiographers, Wright, London, Clinical Radiology,

vol.40, issue.3, pp.276, May 1989.

[15] J. E. Martin, M. Moskowitz, and J. R. Milbrath, “Breast cancer

missed by mammography,” Am. J. Roentgeno., Vol.132, pp.

737-739, May 1979.

[16] R. E. Bird, T. W. Wallace, and B. C. Yankaskas, “Analysis of

cancers missed at screening mammography,”Radiol. , vol.

184, pp. 613-617, Sept. 1992.

[17] M. G. Wallis, M. T. Walsh, and J. R. Lee, “A review of false

negative mammography in a symptomatic population,” Clin.

Radiol. , vol. 44, pp. 13-15, 1991.

[18] F. Shtem, C. Stelling, B. Goldberg, and R. Hawkins, “Novel

technologies in breast imaging: National Cancer Institute

perspective,” in Second Post-Graduate Course Syllabus, Soc.

of Breast Imaging, Orlando , pp. 153-156, May 1995

[19] M. Shenton, R. Kikinis, F. Jolesz, et al. Abnormalities of the

Left Temporal Lobe and Thought Disorder in Schizophrenia. N.

Engl. J. Med,vol.327, pp. 604 - 612, August 1992.

[20] K. Hohne et al. A framework for the Generation of 3D

Anatomical Atlases. SPIE, Visualization in Biomedical

Computing, Vol. 1808,pp.169-213, 1992.

149

[21] R. Kikinis, F.A. Jolesz, W.E. Lorensen, H.E. Cline, P.E Stieg,

Black 3d Reconstruction of Skull Base Tumors from MRI Data

for Neurosurgical Planning. In Proceedings of the Society of

Magnetic Resonance in Medicine Conference, 1991.

[22] R. Kikinis, D. Altobelli, W. Lorensen, W. Wells, and G.

Ettinger. Pre- and intraoperative tumor localization using 3d

renderings of mri's. 12th Annual Scientific Meeting of the

Society of Magnetic Resonance in Medicine, 1993.

[23] D. Levin, X. Hu, K. Tan, et al. The Brain: Integrated Three-

Dimensional Display of MR and PET Images. Radiology,

vol.172, pp.783-789 1989.

[24] J. Belliveau, D. Kennedy, R. McKinstry, et al. Functional

Mapping of the Human Visual Cortex by Magnetic Resonance

Imaging. Science,vol.254 , pp, 716 -719, November 1991.

[25] M. Vannier, D. Jordan, W. Murphy, Multi-Spectral Analysis of

Magnetic Resonance Images. Radiology,vol.154, pp.221-224,

1985.

[26] M. Kohn, N. Tanna, G. Herman, et al. Analysis of Brain and

Cerebrospinal Fluid Volumes with MR Imaging.

Radiology,vol.178, pp. 115 -122, 1991.

[27] R.B. Lufkin, T. Sharpless, B. Flannigan, and W. Hanafee.

Dynamic-Range Compression in Surface-Coil MRI. AJR,vol.11,

pp.379 -382, 1990.

150

[28] L. Axel, J. Costantini, and J. Listerud. Intensity Correction in

Surface-Coil MR Imaging. AJR,vol.148, pp.418 - 420, 1987.

[29] K.O. Lim and A. P. erbaum. Segmentation of MR Brain Images

into Cerebrospinal Fluid Spaces, White and Gray Matter.

JCAT,vol.134, pp.588 -593, 1989.

[30] B. Dawant, A. Zijdenbos, and R. Margolin. Correction of

Intensity Variations in MR Images for Computer- Aided Tissue

Classification. IEEE Trans. Med. Imaging,vol.12,issue.4,

pp.770 -781, 1993.

[31] J. Gohagan, E. Spitznagel, W. Murphy, M. Vannier,

Multispectral Analysis of MR Images of the Breast.

Radiology,vol.163,pp. 703 -707, 1987.

[32] S. Aylward and J. Coggins. Spatially Invariant Classification

of Tissues in MR Images. Proceedings of the Third Conference

on Visualization in Biomedical Computing. SPIE, 1994.

[33] Robert M. Haralick, Statistical and Structural Approaches to

Texture, IEEE Proceedings of Vol.67, No.5, May 1979.

[34] Gonzalez RC, Woods RE. Digital image processing. 2nd edition

Pearson Education 2004.

[35] Pratt KW. Digital image processing. 3rd ed. Willey; page no.

551-87, 2001.

[36] Pal NR, Pal SH. A review on image segmentation techniques.

Pattern Recognition; vol.26, pp. 1277-94, 1993.

151

[37] Withey DJ, Koles ZJ. Three generations of medical image

segmentation: Methods and available software. International

Journal on Bioelectromag,vol.9,pp.67-68, 2007.

[38] Pham DL, Xu C, Prince JL. Current methods in medical image

segmentation. Ann Rev Biomedical Engg., vol.2, pp.315-37,

2000.

[39] Ramesh N, Yoo JH, Sethi IK. Thresholding based on histogram

approximation. IEEE Proc Vision Image Signal Proc, vol.142,

pp.271-279, 1995.

[40] Lai CC. A novel image segmentation approach based on

particle swarm optimization. IEICE Transaction Fundamentals;

vol.E89-A issue. 1,pp.324-327, 2006.

[41] Brink AD. Minimum spatial entropy threshold selection. IEEE

Processing Vision Image Signal Proc.vol.142, pp.128-132,

1995.

[42] Frank RJ, Grabowski TJ, Damasio H. Voxelvise percentage

tissue segmentation of human brain Magnetic resonance

images. In Abstracts, 25th Annual meeting, Society for

Neuroscience, Society for Neuroscience. Washington, DC: pp.

694, 1995.

[43] D. Brzakovic, X. M. Luo, and P. lBzrakovic, “An approach to

automated detection of tumors in mammography,” IEEE

Trans. Med. Imag., Vol.9, No.3, pp. 233-241, Sept. 1990.

[44] F. F. Yin, M. L. Giger, K. Dol, C. E. Metz, R. A. Vyborny, and C.

J. Schmidt, “Computerized detection of masses in digital

152

mammograms: Analysis of bilateral subtraction images,‟‟ Med.

Phys., vol. 18, no. 5, pp. 955-963, Sept. 1991.

[45] T. K. Lau and W. F. Bischof, “Automated detection of breast

tumors using the asymmetry approach,‟ Comput. Biomed.

Res., vol. 24, pp.273-295, 1991.

[46] H. P. Chan and K. Doi, S. Galhotra, C. J. Vyborny, H.

MacMahon, and P. M. Jokich, “Image feature analysis and

computer-aided diagnosis in digital radiography. I. Automated

detection of microcalcifications in mammography,” Med.

Phys., vol. 14, pp. 538–548, 1987.

[47] D. H. Davies, D. R. Dance, and C. H. Jones, “Automatic

detection of microcalcifications in digital mammograms using

local area thresholding techniques,” SPIE Med. Imag., vol.

1092, no. 3, pp. 153–159, 1989.

[48] T. Soni, J. R. Zeidler, and W. H. Ku, “Performance evaluation

of 2-D adaptive prediction filters for detection of small objects

in image data,” IEEE Trans. Image Processing, vol. 2, no. 3,

pp. 327–339, July 1993.

[49] W. Qian, L. P. Clarke, M. Kallergi, H. Li, R. Velthuizen, R. A.

Clark, and M. L. Silbiger, “Tree-structured nonlinear filter and

wavelet transform for microcalcification segmentation in

mammography,” SPIE Biomed. Image Processing and Biomed.

Visual. , Vol. 1905, pp. 509–520, 1993.

[50] J. Dengler, S. Behrens, and J. F. Desaga, “Segmentation of

microcalcifications in mammograms,” IEEE Trans. Med. Imag.,

Vol. 12, no. 4, pp. 634–642, Dec. 1993.

153

[51] D. Zhao, “Rule-based morphological feature extraction of

microcalcifications in mammograms,” SPIE Med. Imag., Vol.

1095, pp. 702–715, 1993.

[52] S. C. Lo, H. P. Chan, J. S. Lin, H. Li, M. T. Freedman, and S.

K. Mun, “Artificial convolution neural network for medical

image pattern recognition,” Neural Networks, Vol. 8, no. 7/8,

pp. 1201–1214, 1995.

[53] W. Zhang, K. Doi, M. L. Giger, Y. Wu, R. M. Nishikawa, and R.

A. Schmidt, “ Computerized detection of clustered

microcalcifications in digital mammograms using a shift-

invariant artificial neural network,” Med. Phys., Vol. 21, no. 4,

pp. 517–524, 1994.

[54] Y. Wu, K. Doi, M. L. Giger, and M. Nishikawa, “Computerized

detection of clustered microcalcifications in digital

mammograms: Application of artificial neural networks,” Med.

Phys., Vol.19, pp. 555–560, 1992.

[55] N. Karssemeijer, “A stochastic model for automated detection

of calcifications in digital mammograms,” Proc. 12th Int. Conf.

IPMI, Wye, UK, pp. 227–238, 1991.

[56] N. Karssemeijer, “Recognition of clustered microcalcifications

using a random field model,” SPIE Med. Imag., Vol. 1905, pp.

776–786, 1993.

[57] F. Lefebvre, H. Benali, R. Gilles, E. Kahn, and R. D. Paola, “A

fractal approach to the segmentation of microcalcification in

154

digital mammograms,” Med. Phys., Vol.22, No.4, pp. 381–

390, 1995.

[58] D. Brzakovic, P. Brzakovic, and M. Neskovic, “An approach to

automated screening of mammograms,” SPIE Biomed. Image

Processing, Biomed. Visual. , Vol. 1905, pp. 690–701, 1993.

[59] H. Yoshida, K. Doi, and R. M. Nishikawa, “Automated

detection of clustered microcalcifications in digital

mammograms using wavelet transform techniques,” SPIE

Image Processing, Vol. 2167, pp. 868–886, 1994.

[60] A. F. Laine, S. Schuler, J. Fan, and W. Huda, “Mammographic

feature enhancement by multiscale analysis,” IEEE Trans.

Med. Imag., Vol.13, No.4, pp. 725–740, Dec. 1994.

[61] Huai Li, K.J. Ray Liu,Shih Chung B.Lo, “Fractal Modeling and

Segmentation for the Enhancement of microcalcifications in

Digital Mammograms,” IEEE Transactions on Medical imaging,

Vol.16,issue.6 ,page no.785-798, December 1997.

[62] M.I. Rajab, M.S. Woolfson, and S.P. Morgan, “Application of

region-based segmentation and neural network edge

detection to skin lesions,” Computerized Medical Imaging and

Graphics, vol. 28, pp. 61–68, 2004.

[63] H. Tang, E.X. Wu, Q.Y. Ma, D. Gallagher, G.M. Perera, and T.

Zhuang, “MRI brain image segmentation by multi-resolution

edge detection and region selection,” Computerized Medical

Imaging and Graphics, vol. 24, pp. 349–357, 2000.

155

[64] Huertas, A. and Medioni, G., “Detection of intensity changes

with sub pixel accuracy using Laplacian-Gaussian masks,”

IEEE Trans. On Pattern Analysis and Machine Intelligence,

PAMI, Vol. 8, pp. 651–664, 1986.

[65] S. M. Lai, X. Li, and W. F. Bischof, “On techniques for

detecting circumscribed masses in mammograms,” IEEE

Trans. Med. Imag., Vol.8, No. 4, pp. 377-386, Dec. 1989.

[66] W. Qian, L. P. Clarke, M. Kallergi, and R. A. Clark, “Tree-

structured nonlinear filters in digital mammography”, IEEE

Trans. Med. Imag., vol.13, no. 1, pp. 25-36, Mar. 1994.

[67] Hancock ER, Kittler J. Edge labeling using dictionary-based

relaxation. IEEE Trans PAMI vol.12, pp. 165-181, 1990.

[68] Liow YT. A contour tracing algorithm that preserves common

boundaries between regions. CVGIP Image Understanding;

vol.53, pp.313-21, 1991.

[69] Law T, Itoh H, Seki H. Image filtering, edge detection, and

edge tracing using fuzzy reasoning. IEEE Trans PAMI,

vol.18,pp. 481-491, 1996.

[70] Ney H. A comparative study of two search strategies for

connected word recognition Dynamic programming and

heuristic search. IEEE Trans PAMI, vol.14, pp.586-595, 1992.

[71] Xu L, Oja E. Randomized Hough transform (RHT): Basic

mechanism and algorithms, and computational complexities.

CVGIP Image Comput, vol.57, pp. 131-54, 1993.

156

[72] Kalvian H, Hirvonen P, Xu L, Oja E. Probabilistic and non-

probabilistic Hough transform: Overview and comparisons.

Image Comput, vol.13, pp.239-52, 1995.

[73] P.F. Felzenszwalb and D.P. Huttenlocher, "Efficient Graph-

Based Image Segmentation," International Journal of

Computer Vision, Vol.59, No.2, 2004.

[74] S. W. Zueker, “Region growing: Childhood and adolescence”

Computer Graphics and Image Processing,vol.5, pp.382-

399,1976.

[75] Chowdhury M. I. and Robinson J. A., “Improving Image

Segmentation Using Edge Information,” Proceedings of the

1st IEEE Conference on Electrical and Computer Engineering,

Halifax, Canada, vol.1, pp. 312-316, 2000.

[76] Yu Y. and Wang J., “Image Segmentation Based on Region

Growing and Edge Detection,” Proceedings of the 6th IEEE

International Conference on Systems, Man and Cybernetics,

Tokyo, Vol.6., pp. 798-803, 1999.

[77] Tang H., Wu E. X., Ma Q. Y., Gallagher D., Perera G. M., and

Zhuang T., “MRI Brain Image Segmentation by Multi-

Resolution Edge Detection and Region Selection,”

Computerized Medical Imaging and Graphics, Vol.24, No.6,

pp. 349-357, 2000.

[78] W. P. Kegelmeyer Jr., J. M. Pruneda, P. D. Bourland, A. Hillis,

M. W. Riggs, and M. L. Nipper, “Computer-aided

mammographic screening for spiculated lesions,” Radiol., vol.

191, no. 2, pp. 331-337, May 1994.

157

[79] D. Marr and E. Hildreth, “Theory of edge detection,” in

Proceeding Royal Society, London., Vol.207, pp. 187-217 ,

1980.

[80] J. Lunscher and M. P. Beddoes, “Optimal edge detector

design: Parameter selection and noise effects,” IEEE Trans.

Pattem Anal. Machine Intell. , Vol.8, No.2, pp. 154-176, Mar.

1986.

[81] Julsez B. Textons, the element of texture perception and their

interactions. Nature , vol.290, pp. 91-97, 1981.

[82] Tesar L, Shimizu A, Smutek D, Kobatake H, Nawano S.

Medical image analysis of 3D CT images based on extension of

Haralick texture features. Comput Med Imaging Graphics,

vol.32, pp.513-20, 2008.

[83] Prasad M, Brown MS, Abtin F, Vasunilsahorn S, Goldin JG.

Comparison of scleroderma detection techniques in CT images

using supervised and unsupervised methods. International

Journal of Biomedical Engg. Technology, vol.1, pp.4, 2008.

[84] Koss JE, Newman FD, Johnson TK, Krich DL. Abdominal organ

segmentation using texture transform and Hopfield neural

network. IEEE Trans Med Image, vol.18,pp. 640-648, 1999.

[85] Kimme, C., O'Loughlin B.J., and Sklansky, J., " Automatic

detection of suspicious abnormalities in breast radiographs",

Proceedings of IEEE Conference on Computer Graphics,

Pattern Recognition and Data Structure, pp.84-88, 1975.

158

[86] Chan, H.-P., Wei, D.T., Helvie, M.A., Sahiner B., et al.

"Computer-aided classification of mammographic masses and

normal tissue: linear discriminate analysis in texture feature

space", Phs. Med. Bio.vol.40, pp. 857-876, 1995.

[87] Chivers, R., Davies, IJ., and Duarte, F.M., "Perceptual studies

on ultrasonic B-scan textures", Phys. Med Biol. Vol.31, No.6,

pp.627-634, 1986.

[88] Garra, B.S. Insana, M.F., et al, "Quantitative ultrasonic

detection and classification of diffuse liver disease comparison

with human observer performance", Investigate Radiology,

vol.24, NO.3, pp. 196-203, 1989.

[89] Garra, B.S., Insana, M.F., etc, "Quantitative ultrasonic

detection of arachymal structural change in diffuse renal

disease", Investigative Radiology, vol.29, No.2, pp.134-140,

1994.

[90] Bergener, I., Pelikan, E., Tolxdorff T., and Repges, R., "X-ray

segmentation using a texture-based wavelet approach",

H.U.Lemke, (Eds) CAR'95, pp. 125-130, Springer- Verlag,

Berlin, 1995.

[91] Scholl, I., Pelikan, E., Repges, R., and Tolxdorft T., "Texture-

based feature extraction using the wavelet transform on X-

rays", In press, 1996.

[92] Tuceryan, M. and Jam, A.K., "Texture Analysis", in C.H.Chen,

L.F.Pau and P.S.P. Wang Eds. Handbook of Pattern

Recognition and Computer Vision, World Scientific Publishing

Company, pp. 235-276, 1994.

159

[93] Bergen, J. R., "Theories of visual texture perception", D.

Regan Eds. Spatial Vision, CRC Press, pp.114-134, 1991.

[94] Simchony, T. and Chellappa, "Stochastic and deterministic

algorithms for MAP texture segmentation' IEEE ICASSP'88,

pp. 1120-1123, 1998.

[95] Hu It and Fahmy, M.M., "texture segmentation based on a

hierarchical Markov random field model' Signal Processing,

vol.26, pp.285-305, 1992.

[96] Lee. H.J. and Lee N.I., "A fast and adaptive method to

estimate texture statistics by the spatial gray level

dependence matrix(SGLDM) for texture image segmentation",

Pattern Recognition Letters, vol.13, issue.4, pp.291-303,

1992.

[97] Chellappa, R. and Kashyap, R.L., "Model-based texture

segmentation and classification", In C.H.Chen et al

Eds.Handbook of Pattern Recognition and Computer Vision,

pp. 277-310,1995.

[98] Schofield, A.J. and Foster, D.H., "Artificial neural networks

simulating visual texture segmentation and target detection in

line element images", Phil. Trans. K Soc. Lond. B., vol.350,

pp.401-412, 1995.

[99] Speis, A. and Healey, G., "Feature extraction for texture

discrimination via random field models with random spatial

interaction", IEEE Trans. Image Processing, Vol.5, No.4, pp.

635-645, 1996.

160

[100] Tremeau, A., Bousigue, J., and Laget, B., "Co-occurrence

shape descriptors applied to texture classification and

segmentation", SP1E Vol. 2665, pp. 135-147, 1996.

[101] Bouman, C. and Liu, Bede, "segmentation of textured images

using a multiple resolution approach", IEEE proceedings of

ICASSP'88, pp. 1124-1127, 1988.

[102] Unser, M. and Eden, M., "Multiresolution feature extraction

and selection for texture segmentation", IEEE Trans. On

Pattern Analysis and Machine Intelligence, Vol.11, No.7, pp.

717-728, 1989.

[103] Blostem D. and Ahuja N., "shape from texture element

extraction and surface estimation", IEEE Trans. on Pattern

Analysis and Machine Intelligence , Vol.11, No.12, pp. 1233-

1251, 1989.

[104] Zhu, Y.M. and Goutte R., "Analysis and comparison of

space/spatial frequency and multiscale methods for texture

segmentation", Optical Engineering, Vol.34, No.1, pp.269-

282, 1995.

[105] Xie, Z.-Y., Brady, M., "Texture segmentation using local

energy in wavelet scale space", in Buston, B. and Cipolla R.

Eds, Computer Vision, ECC V'96 Springer-Verlag, pp. 304-

313, 1996.

[106] Dr. H. B. Kekre, Saylee Gharge, “Selection of Window Size for

Image Segmentation using Texture Features,” Proceedings of

International Conference on Advanced Computing

161

&Communication Technologies(ICACCT-2008) Asia Pacific

Institute of Information Technology SD India, Panipat, 08-09

November,2008.

[107] Dr. H. B. Kekre, Saylee Gharge, “Image Segmentation of MRI

using Texture Features,” Proceedings of International

Conference on Managing Next Generation Software

Applications ,School of Science and Humanities, Karunya

University, Coimbatore, Tamil Nadu, 05-06 December, 2008.

[108] Dr. H. B. Kekre, Saylee Gharge, “SAR Image Segmentation

using co-occurrence matrix and Slope Magnitude,”

Proccedings of International Conference on Advanced

Computing ,Communication & Control (ICAC3-2009), Fr.

Conceicao Rodrigues College of Engineering in co-operation

with Association for Computing Machinery, Mumbai , 23-24

January,2009.

[109] Dr. H. B. Kekre, Saylee Gharge, “Statistical Parameters like

Probability and Entropy applied to SAR image segmentation,”

International Journal of Engineering Research & Industry

Applications (IJERIA), Vol.2, No.IV, pp.341-353, 2009.

[110] Dr. H.B.Kekre, Tanuja Sarode, Saylee Gharge, “Tumor

Demarcation of Mammography Images using Entropy with

Different Window Sizes” Second International Conference on

Emerging Trends in Engineering and Technology, ICETET-

2009, Raisoni College of Engineering, Nagpur, pp,889-894,

Dec2009,,ieeecomputersociety.org/10.1109/ICETET.2009.228

[111] Dr. H. B. Kekre, Saylee Gharge, “Segmentation of MRI

Images using Probability and Entropy as Statistical

162

parameters for Texture analysis,” Advances in Computational

sciences and Technology (ACST) ,Volume 2, No.2, pp: 219-

230, 2009, http://www.ripublication.com

[112] Dr. H.B. Kekre , Saylee Gharge, “MRI Image Segmentation

using Variance through GLCM and Direct Method,” in National

Level Technical Paper Presentation Competition organized in

association with Siemens & PCS Technology (THINKQUEST-

2009) organized by Babasaheb Gawde Institute of

Technology, Mumbai on 14th March 2009 .

[113] Jain AK, Duin RP, Mao J. Statistical pattern recognition: A

review. IEEE Trans PAMI, vol. 22, pp 4-37,2000.

[114] Frigui H, Krishnapuram R. A robust competitive clustering

algorithm with application in computer vision. IEEE Trans

PAMI, vol.21,pp.450-465, 1999.

[115] Tseng LY, Yang SB. A genetic approach to the automatic

clustering problem. Patten Recognition,vol.34, pp.415-424,

2001.

[116] Bandyopadhyay S. Simulated annealing using a reversible

jump markov chain monte carlo algorithm for fuzzy clustering.

IEEE Trans Knowledge Data Engg., vol.17, pp. 479-90, 2005.

[117] A. K .Jain, M. N. Murty and P .T. Elynn, Data Clustering:

Review. ACM: Computing surveys. Vol.31,issue.3, pp.264-

323, 1999.

[118] J.C. Bezdek.Pattern Recognition with Fuzzy objective Function

Algorithms, Plenum Press. New york, 1981.

163

[119] P.Mckenzic and M.Alder.Initializing the EM algorithm for the

use in Gaussian mixture modeling. Proc.on Pattern

Recognition in practice IV, pp.91-105, 1994.

[120] A.P.Dempster, N.M. Laird, and D.B.Rubin.Maximum likelihood

from incomplete data via EM algorithm. Journal of the Royal

statistical Society: Series B, vol.39, issue.1, pp.1-38, 1997.

[121] R.P.Velthnizer.Computer diagnosis of mammographic masses.

Proc. of the App. Imagery Pattern Recognition Workshop,

page 166-172, 2000.

[122] C.H.Chen,G.G.Lee, On digital mammogram segmentation and

microcalcification detection using Multiresolution wavelet

analysis, Graphical Models and Image Processing Vol. 59,issue

5, pp. 349-364, 1997.

[123] L.Zhang and A.K.Chan. An artificial intelligent algorithm for

tumor detection in screening mammograms. IEEE Transaction

on Medical Imaging vol.20, issue 7, pp.559-567,2001.

[124] Jeng-Shyang Pan, Zhe-Ming Lu, Sheng-He Sun, “An Efficient

Encoding Algorithm for Vector Quantization Based on

Subvector Technique”, IEEE Transactions on image

processing, vol. 12, issue. 3, March 2003.

[125] R. M. Gray, “Vector quantization”, IEEE ASSP Magz.,vol.1, pp.

4-29, April 1984.

164

[126] Y. Linde, A. Buzo, R. M. Gray, “An algorithm for vector

quantizer design”, IEEE Trans. Commun., vol. COM-28, no. 1,

pp. 84-95, 1980.

[127] J. B. MacQueen, “Some Methods for Classification and

Analysis of Multivariate Observations”, Proceedings of 5-th

Berkeley symposium on Mathematical Statistics and

Probability”, Berkely, University of California Press, vol. 1, pp.

281-297, 1967.

[128] C.K. Chan, L.-M. Po, “A complexity reduction technique for

image vector quantization”, IEEE Trans. Image Process. vol.

3, pp.312–321, 1992.

[129] H.B.Kekre, Tanuja K. Sarode, “New Fast Improved Codebook

Generation Algorithm for Color Images using Vector

Quantization” International Journal of Engineering and

Technology, vol. 1, pp.67-77, Sept. 2008.

[130] H.B.Kekre, Tanuja K. Sarode, “Fast Codebook Generation

Algorithm for Color Images using Vector Quantization”

International Journal of Computer Science and Information

Technology (IJCSIT), volume 1, issue. 1, pp.7-12, Jan-June

2008.

[131] H.B.Kekre, Tanuja K. Sarode, “An Efficient Fast Algorithm to

Generate Codebook for Vector Quantization”, First

International Conference on Emerging Trends in Engineering

and Technology, pp.62-67, 16-18 July 2008, IEEE Xplore,

ACM Portal.

165

[132] H. B. Kekre, Tanuja K. Sarode, “New Fast Improved

Clustering Algorithm for Codebook Generation for Vector

Quantization”, International Conference on Engineering

Technologies and Applications in Engineering, Technology and

Sciences, Computer Science Department, Saurashtra

University, Rajkot, Gujarat. (India), Amoghsiddhi Education

Society, Sangli, Maharashtra (India), 13th – 14th January

2008.

[133] Momotaz Begum, Nurun Nahar, Kaneez Fatimah, M. K. Hasan,

M. A. Rahaman, “An Efficient Algorithm for Codebook Design

in Transform Vector Quantization”, WSCG‟2003, February 3-7,

2003.

[134] Robert Li and Jung Kim, “Image Compression Using Fast

Transformed Vector Quantization”, Proceedings 29th Applied

Imagery Pattern Recognition Workshop, pp.141 – 145, 2000.

[135] Zhibin Pan; Kotani, K.; Ohmi, T., “Enhanced fast encoding

method for vector quantization by finding an optimally-

ordered Walsh transform kernel”, IEEE International

Conference Image Processing (ICIP 2005), vol. 1, pp.573-

576, 11-14 Sept. 2005.

[136] J.B.MacQueen. Some methods of classification and analysis of

multivariate observations. In Proc. of the fifth Berkeley

Symposium on Mathematical statistics and probability, vol. 1,

pp. 281-297, 1967.

[137] B.Sahiner, H.P.Chan, N.Petric, M.A. Helvie, and M.M. Goodsit.

Computerised characterization of masses on mammograms:

166

The rubber band straightening transform and Texture analysis

Medical Physics, vol.25, issue 4,pp.516-526, 1998.

[138] B.Sahiner, H.P.Chan, N.Petric, M.A. Helvie, M.M. Goodsit and

D.D.Adler. Image feature selection by a genetic algorithm:

Application to classification of mass and normal breast tissue.

Medical Physics, vol.23, pp. 1671-1684,1996.

[139] L.Li, R.A.Clark and J.A.Thomas. Computer-aided diagnosis of

masses with full field digital mammography. Academic

Radiology, vol.9,issue. 1, pp.4-12,2002.

[140] Lloyd, S. Least Squares Quantization in PCM‟s. Bell Telephone

Laboratories Paper, Murray Hill,NJ,1957

[141] Pal, N., Bezdek, J., & Tsao, E. (). Generalized Clustering

Networks and Kohonen‟s Self Organizing Scheme. IEEE

Transaction on Neural Networks, vol.4, 549–557, 1993.

[142] Xiaowei Zheng, Bryant A. Julstrom and Weidong Cheng,

“Design of Vector Quantization Codebooks using a Genetic

Algorithm”,Proceedings of IEEE International Conference on

Evolutionary Computation (ICEC'97), pp.525-529, 1997.

[143] Dr. H.B.Kekre, Saylee Gharge, “Tumor Detection in

Mammography Images using Vector Quantization Technique”

International Joint Journal conference on computer and

communication technology (IJJCCT 2009) held in Shanghai,

China, Dec 28-29,2009 published by International Journal of

Intelligent Information Technology Application(IJIITA).

www.engineering-press.org/journals/ijiita/n2/v5/237.pdf

167

[144] Dr. H.B.Kekre, Tanuja Sarode, Saylee Gharge, “Vector

Quantization for Tumor Demarcation of Mammograms”

International conference on Recent Trends in Business

Administration and Information Processing (BAIP-2010)

Trivandrum, Kerala ,March 26-27,2010 available on Springer

LNCS digital library .

[145] Dr. H.B.Kekre, Tanuja Sarode, Saylee Gharge, “Kekre‟s Fast

Codebook Generation Algorithm for Tumor Detection in

Mammography Images” International conference and

workshop on Emerging Trends in Technology (ICWET 2010) in

co-operation with ACM SIGART and SIGARCH USA, 26th &27th

,Feb-2010.portal.acm.org/citation.cfm?id=1742080

[146] H. B. Kekre, Tanuja K. Sarode, Saylee Gharge, “Image

Segmentation of MRI Images using Vector Quantization

Techniques” International Conference on Contours of

Computing Technology, 13th-14th March 2010, at Babasaheb

Gawde Institute of Technology published by Springer Link

Digital Library ISBN 978-81-8489-988-2.

[147] H. B. Kekre, Tanuja K. Sarode, Saylee Gharge, “Image

Segmentation of mammographic images using Kekre‟s

Proportionate Error Technique on Probability Images”

published in International Journal of Computer and Electrical

Engineering (IJCEE),2010 (Selected).

[148] H. B. Kekre, Tanuja K. Sarode, Saylee Gharge, “Tumor

Demarcation in Mammographic Images using Vector

Quantization Technique on Entropy Images” International

Conference on Contours of Computing Technology, 13th-14th

March 2010, at Babasaheb Gawde Institute of Technology

168

published by Springer Link Digital Library ISBN 978-81-8489-

988-2 .

[149] Dr. H. B. Kekre, Saylee Gharge, “Segmentation of MRI

Images using Probability and Entropy as Statistical

parameters for Texture analysis,” Advances in Computational

sciences and Technology (ACST) ,Volume 2, No.2, pp: 219-

230, 2009, http://www.ripublication.com

[150] Dr. H. B. Kekre, Tanuja K. Sarode, Saylee Gharge, “Detection

and Demarcation of Tumor using Vector Quantization in MRI

Images”, International Journal of Engineering Science and

Technology (IJEST), vol. 2, issue.2, pp.59-66, 2009.

www.ijest.info/docs/IJEST10-02-08-73.pdf .

[151] Dr. H. B. Kekre, Tanuja K. Sarode, Saylee Gharge,

“Performance Evaluation of KFCG and LBG Algorithms in

Tumor Demarcation of Mammograms" in International

Congress for global science and technology (ICGST), Egypt,

vol.10,issue.1,pp.47-50,December2010.

www.icgst.com/bime/Volume10/Issue1/P1160949925.pdf

[152] Yuval, F.. Fractal image compression (theory and application).

Institute for non-linear Science, University of California, San

Diego, USA, 1996.

[153] Canny, J. F. A computational approach to edge detection.

IEEE Trans Pattern Analysis and Machine Intelligence, vol.8,

issue. 6, pp. 679-698, 1986.

[154] Folorunso O., Vincent, O. R., & Dansu, B. M.. Image edge

detection: A knowledge management technique for visual

169

scene analysis. Information Management and Computer

Security, vol.15,issue.1, pp. 23-32, 2007.

[155] N. Senthilkumaran and R. Rajesh, “Edge Detection Techniques

for Image Segmentation - A Survey”, Proceedings of the

International Conference on Managing Next Generation

Software Applications (MNGSA-08), 2008, pp.749-760.

[156] Mantas Paulinas and Andrius Usinskas, “A Survey of Genetic

Algorithms Applications for Image Enhancement and

Segmentation”, Information Technology and Control,

vol.36,issue.3, 2007, pp.278-284.

[157] Vladimir Vilkov, “Thresholding for Segmentation and

Extraction of Extensive Objects on Digital Images”, Springer

Berlin volume 5803,pp. 623-630,2009.

[158] Satya Swaroop Pradhan, Dipti Patra, “Adaptive Thresholding

based Image Segmentation with Uneven Lighting condition”,

IEEE Region Colloquium and Third ICIIS, Kharagpur,

December 8-10, 2008.

[159] Walter Jenten, Lutz Freudenberg, “Segmentation of PET

Volumes by Iterative Image Thresholding”, Journal of Nuclear

Medicine vol. 48 No.1,pp-108-114,2007.

[160] Terrence Chen, Thomas S. Huang, “Region based Hidden

Markov Random Field Model for Brain MR Image

Segmentation”, World Academy of science, Engineering and

Technology,vol.4, pp.233-236, 2005.

170

[161] Vongyue Zhang, Michael Brady, “Segmentation of Brain MR

Images through Hidden Markov Random Field Model and the

Expectation Maximization Algorithm”, IEEE Transactions on

Medical imaging, vol.20,I ssue.1, Jan-2001.

[162] Andrey, P.Tarroux, “Unsupervised Segmentation of MRF

modeled Textured Images using Selectionist relaxation”,

Pattern Analysis and Machine Intelligences, IEEE Transactions,

vol.20, issue-3, pp-252-262, March 1998.

[163] Jun L. Chen, Steve R. Gunn, Mark S., Markov Random Field

Models for Segmentation of PET Images, Spring

Berlin/Heidelberg, vol. 2082, pp-468-474,2001.

[164] M. Unser, “Texture classification and segmentation using

wavelet frames,” IEEE transaction on Image Processing, vol.

4, No. 11, pp. 1549–1560, 1995.

[165] B. Wang and L. Zhang, “Supervised texture segmentation

using wavelet transform,” Proceedings of the 2003

International Conference on Neural Networks and Signal

Processing, vol. 2, pp. 1078–1082, 2003.

[166] Chang-Tsun Li and Roland Wilson, “Unsupervised texture

segmentation using multiresolution hybrid genetic algorithm,”

Proc. IEEE International Conference on Image Processing

ICIP03, pp. 1033–1036, 2003.

[167] T. R. Reed and J. M. H. Du Buf, “A review of recent texture

segmentation, feature extraction techniques,” in CVGIP Image

Understanding, vol.57, pp. 359–372, 1993.

171

[168] A. K. Jain and K. Karu, “Learning texture discrimination

masks,” IEEE transactions of Pattern Analysis and Machine

Intelligence, Vol. 18, No. 2, pp. 195–205, 1996.

[169] Eldman de Oliveira Nunes and Aura Conci, “Texture

segmentation considering multi band, multi resolution and

affine invariant roughness,” SIBGRAPI, pp. 254–261,2003,.

[170] L.F. Eiterer, J. Facon, and D. Menoti, “Postal envelope address

block location by fractal-based approach,” 17th Brazilian

Symposium on Computer Graphics and Image Processing, D.

Coppersmith, Ed., pp. 90–97,2004, .

[171] Haindl, M. & Mikeš, S. Colour texture segmentation using

modelling approach. Lecture Notes in Computer Science,

vol.3687,pp. 484–491, 2005.

[172] Haindl, M. & Mikeš, S. Unsupervised texture segmentation

using multispectral modelling approach. In Y. Tang, S.Wang,

D. Yeung, H. Yan, & G. Lorette (Eds.), Proceedings of the 18th

International Conference on Pattern Recognition, ICPR, Los

Alamitos: IEEE Computer Society. vol.2, pp. 203–206, 2006.

[173] Haindl, M. & Mikeš, S. Unsupervised texture segmentation

using multiple segmenters strategy. In M. Haindl, J. Kittler, &

F. Roli (Eds.), MCS 2007, Lecture Notes in Computer Science

vol.4472,pp. 210–219,2007

[174] Robert M. Haralick, Statistical and Structural Approaches to

Texture, IEEE Proceedings,vol. 67, issue.5,pp.786-804, May

1979.

172

[175] Thomas P., William E., “Efficient Gabor Filter Design for

Texture segmentation,” Pattern Recognition 1996, Pattern

Rec.Soc.

[176] Ojala, T. & Pietikainen, M. Unsupervised texture segmentation

using feature distributions. Pattern Recognition, vol.32,

pp.477-486, 1999.

[177] Hirokazu Nosto, “Histopathological Diagnostic Support

Technology using Higher Order Local Autocorrelation

Features”, Symposium on Bio-inspired Learning and

Intelligent systems for Security, pp-61-65, 2009.

[178] D. Michael and J. Houchin, “Automatic EEG analysis :A

Segmentation procedure based on autocorrelation on

function”, Electroencephalography and clinical

Neurophysiology, vol. 46, issue.2, pp-232-235, Feb-1979,

[179] Serge Beucher and Christian Lantuejoul, “Use of watersheds

in contour detection”. International workshop on image

processing, real-time edge and motion detection, 1979.

[180] Leila Shafarenko and Maria Petrou, “Automatic Watershed

Segmentation of Randomly Textured Color Images”, IEEE

Transactions on Image Processing, vol.6,issue.11, pp.1530-

1544, 1997.

[181] Serge Beucher and Fernand Meyer. The morphological

approach to segmentation: the watershed transformation. In

Mathematical Morphology in Image Processing, pp. 433-

481,1993.

173

[182] Basim Alhadidi, Mohammad H. et al, “Mammogram Breast

Cancer Edge Detection Using Image Processing Function”

Information Technology Journal vol.6, issue.2, pp.217-221,

,ISSN-1812-5638,2007 .

[183] C.E. Shannon, "A Mathematical Theory of Communication”,

Bell System Technical Journal, vol.27, pp.379-423,July

October, 1948.

[184] Dr. H.B.Kekre, Saylee Gharge, “Direct Variance on MRI

Images for Tumor Detection,” in Techno-Path Journal of

Science, Engineering &Technology Management by Mukesh

Patel School of Technology Management & Engineering,

NMIMMS University, Mumbai , volume 3, 2009.

[185] Dr. H.B. Kekre, Saylee Gharge, “MRI Image Segmentation

using Variance through GLCM and Direct Method,” National

Level Technical Paper Presentation Competition organized in

association with Siemens & PCS Technology (THINKQUEST-

2009) organized by Babasaheb Gawde Institute of

Technology, Mumbai on 14th March 2009 .

[186] Yang, M., Ahuja “Gaussian mixture model for human skin

color and its application in image and video databases”, Proc.

of the SPIE: Conf. on Storage and Retrieval for Image and

Video Databases (SPIE 99), Vol. 3656, pp.458–466, 1999.

[187] K. Rose, E. Gurewitz, G. C. Fox, "Vector quantization by

deterministic annealing," IEEE Trans. Information Theory, vol.

38, no. 4, pp. 1249-1257, July 1992.

174

[188] G. Fung and O. L. Mangasarian “Semi-supervised support

vector machines for unlabeled data classification”,

Optimization Methods and Software, vol. 15, pp. 29–44, 2001.

[189] Jeng-Shyang Pan, Zhe-Ming Lu, Sheng-He Sun, “An Efficient

Encoding Algorithm for Vector Quantization Based on

Subvector Technique”, IEEE Transactions on image

processing, vol 12,issue. 3, March 2003.

[190] R. M. Gray, “Vector quantization”, IEEE ASSP Magz., pp.4-29,

April. 1984.

[191] Y. Linde, A. Buzo, R. M. Gray, “An algorithm for vector

quantizer design”, IEEE Trans. Commun., vol. COM-28,vol.1,

pp. 84-95, 1980.

[192] Makur, Anamitra and Subbalakshmi, “Variable Dimension VQ

Encoding and Codebook Design”, IEEE Transactions on

Communications, vol.45 , issue.8, pp. 897-899, 1997.

[193] Qiu chen ,Koji Kotani, “A VQ-Based Fast Face Recognition

Algorithm using Optimized Codebook”, Proceedings of the

2008 International Conference on Wavelet Analysis and

Pattern Recognition, Hong Kong, 30-31 Aug. 2008.

[194] Jim Z.C. Lai, Yi-Ching Liaw, Julie Liu, “A fast VQ codebook

generation algorithm using codeword displacement”, Pattern

Recognition. vol. 41, issue. 1, pp. 315–319, 2008.

175

[195] Y.C. Liaw, J.Z.C. Lai, W. Lo, “Image restoration of compressed

image using classified vector quantization”, Pattern

Recognition. vol. 35, issue. 2, pp. 181–192, 2002.

[196] J.Z.C. Lai, J.Y. Yen, “Inverse error-diffusion using classified

vector quantization”, IEEE Trans. Image Process. vol. 7,

issue.12, pp. 1753–1758, 1998.

[197] N.M. Nasrabadi, Y. Feng, “Image compression using address

vector quantization”, IEEE Trans. Communication. vol. 38,

issue. 12, pp. 2166–2173, 1990.

[198] J. Foster, R.M. Gray, M.O. Dunham, “Finite state vector

quantization for waveform coding”, IEEE Trans. Info. Theory

vol.31,issue.3, pp.348–359, 1985.

[199] T. Kim, “Side match and overlap match vector quantizers for

images”, IEEE Trans. Image Process. vol.1, issue.2, pp. 170–

185, 1992.

[200] J.Z.C. Lai, Y.C. Liaw, W. Lo, “Artifact reduction of JPEG coded

images using mean-removed classified vector quantization”,

Signal Process. vol.82, issue. 10, pp.1375–1388, 2002.

[201] K.N. Ngan, H.C. Koh, “Predictive classified vector

quantization”, IEEE Trans. Image Process. vol.1, issue.3,

pp.269–280, 1992.

[202] C.H. Hsieh, J.C. Tsai, “Lossless compression of VQ index with

search order coding”, IEEE Trans. Image Process. vol.5,

issue.11, pp. 1579–1582, 1996.

176

[203] P.C. Chang, C.S.Yu, T.H. Lee, “Hybrid LMS-MMSE inverse

halftoning technique”, IEEE Trans. Image Process. Vol.10,

No.1, pp. 95–103, 2001.

[204] H. Y. M. Liao, D. Y. Chen, C. W. Su, H. R. Tyan, “Real-time

event detection and its applications to surveillance systems”,

in Proc. IEEE Int. Symp. Circuits and Systems, Kos, Greece,

pp. 509–512, May 2006.

[205] S. Nalagawa, H. Suzuki, “A new speech recognition method

based on VQ-distortion measure and HMM”, IEEE International

Conference on Acoustics, Speech and Signal processing

(ICASSP), vol-2, April 27-30, 1993.

[206] Chin-Chen Chang, Wen-Chuan Wu, “Fast Planar-Oriented

Ripple Search Algorithm for Hyperspace VQ Codebook”, IEEE

Transaction on image processing, vol.16,issue.6, June 2007.

[207] C. Garcia, G. Tziritas, “Face detection using quantized skin

color regions merging and wavelet packet analysis”, IEEE

Trans. Multimedia, vol.1,issue.3, pp.: 264–277, Sep. 1999.

[208] Ahmed A. Abdelwahab, Nora S. Muharram, “A Fast Codebook

Design Algorithm Based on a Fuzzy Clustering Methodology”,

International Journal of Image and Graphics, vol.7,issue.2,

pp. 291-302, 2007.

[209] J. Zheng, M. Hu, “An anomaly intrusion detection system

based on vector quantization”, IEICE Trans. Inf. Syst.,

vol.E89-D,issue.1, pp 201–210, Jan. 2006.

177

[210] H.B. Kekre, Tanuja K. Sarode, “Speech Data Compression

using Vector Quantization”, WASET International Journal of

Computer and Information Science and Engineering (IJCISE),

,vol.2,issue.4, pp.251-254,2008. http://www.waset.org/ijcise.

[211] H.B.Kekre, Tanuja K. Sarode, Bhakti Raul, “Color Image

Segmentation Using Kekre‟s Algorithm for Vector

Quantization”, WASET International Journal of Computer

Science (IJCS), vol.3,issue.4, pp. 287-292, Fall 2008.

http://www.waset.org/ijcs.

[212] H.B.Kekre, Tanuja K. Sarode, Bhakti Raul, “Color Image

Segmentation using Vector Quantization Techniques Based on

Energy Ordering Concept”, International Journal of Computing

Science and Communication Technologies (IJCSCT),

vol.1,issue.2, pp. 164-171, January 2009.

[213] H.B.Kekre, Tanuja K. Sarode, Bhakti Raul, “Color Image

Segmentation Using Vector Quantization Techniques”,

Advances in Engineering Science Sect. C vol.3, pp.: 35-42,

July-September 2008.

[214] H.B.Kekre, Tanuja K. Sarode, Bhakti Raul, “Color Image

Segmentation Using Kekre‟s Fast Codebook Generation

Algorithm Based on Energy Ordering Concept”, Proceedings of

International Conference on Advances in Computing,

Communication and Control (ICAC3), pp.357-362, Jan 2009,

ACM Portal.

[215] H.B.Kekre, Tanuja K. Sarode, Bhakti Raul, “Color Image

Segmentation using Vector Quantization Techniques Based on

Energy Ordering Concept”, International Conference on

178

Advanced Computing & Communication Technology (ICACCT-

2008) Asia Pacific Institute of Information Technology, Panipat

India, 8-9 Nov 2008.

[216] H.B.Kekre, Tanuja K. Sarode, Sudeep D. Thepade, “Image

Retrieval using Color-Texture Features from DCT on VQ

Codevectors obtained by Kekre‟s Fast Codebook Generation”,

ICGST International Journal on Graphics, Vision and Image

Processing (GVIP), vol 9, no. 5, pp.1-8, September 2009,

Available online at http://www.icgst.com/gvip.

[217] H.B.Kekre, Tanuja K. Sarode, Sudeep D. Thepade, “Color-

Texture Feature based Image Retrieval using DCT applied on

Kekre‟s Median Codebook”, International Journal on Imaging

(IJI), vol.2,issue.A09, pp.54-65, Autumn 2009,

www.ceser.res.in/iji.html.

[218] H.B.Kekre, T.K.Sarode, V.A.Bharadi, A A Agrawal, R J Arora ,

M C Nair, “Iris Recognition Using Vector Quantization”,

International Conference on Signal Acquisition and Processing

(ICSAP 2010), Organized by International Association of

Computer Science & Information Technology (IACSIT)

Singapore, held at Banglore, 9-10th February 2010.

IEEEXplorer.

[219] H.B.Kekre, T.K.Sarode, V.A.Bharadi, “Iris Recognition Using

Discrete Cosine Transform and Kekre‟s Fast Codebook

Generation Algorithm”, International Conference and

Workshop on Emerging Trends in Technology (ICWET 2010).

To be held at Thakur College of Engineering and Technology

(TCET), 26 & 27th February 2010, ACM portal.

179

[220] H.B.Kekre, T.K.Sarode, V.A.Bharadi, “Iris Recognition Using

Vector Quantization”, International Conference and Workshop

on Emerging Trends in Technology (ICWET 2010), Thakur

College of Engineering and Technology (TCET), 26 & 27th

February 2010, ACM portal.

[221] H.B.Kekre, T.K.Sarode, V.A.Bharadi, “A Comparative Study of

DCT and Kekre‟s Median Code Book Generation Algorithm for

Face Recognition”, International Conference and Workshop on

Emerging Trends in Technology (ICWET 2010), Thakur College

of Engineering and Technology (TCET), 26 & 27th February

2010, ACM portal.

[222] H.B.Kekre, T.K.Sarode, V.A.Bharadi, “Signature Recognition

using Vector Quantization”, International Conference and

Workshop on Emerging Trends in Technology (ICWET 2010).

To be held at Thakur College of Engineering and Technology

(TCET), 26 & 27th February 2010, ACM portal.

[223] H.B.Kekre, Tanuja K. Sarode, Dhirendra Mishra, “Coloring

Gray Scale Digital Images using Kekre‟s Fast Code Book

Generation Algorithm”, International Conference and

Workshop on Emerging Trends in Technology (ICWET 2010),

at Thakur College of Engineering and Technology (TCET), 26

& 27th February 2010, ACM portal

[224] Momotaz Begum, Nurun Nahar, Kaneez Fatimah, M. K. Hasan,

M. A. Rahaman, “An Efficient Algorithm for Codebook Design

in Transform Vector Quantization”, WSCG‟2003, February 3-7,

2003.

180

[225] Robert Li and Jung Kim, “Image Compression Using Fast

Transformed Vector Quantization”, Proceedings 29th Applied

Imagery Pattern Recognition Workshop, pp.141 – 145, 2000.

[226] Zhibin Pan; Kotani, K.; Ohmi, T., “Enhanced fast encoding

method for vector quantization by finding an optimally-

ordered Walsh transform kernel”, IEEE International

Conference Image Processing (ICIP 2005), vol. 1, pp.573-

576, 11-14 Sept. 2005.

[227] H.B.Kekre, Tanuja K. Sarode, “An Efficient Fast Algorithm to

Generate Codebook for Vector Quantization”, First

International Conference on Emerging Trends in Engineering

and Technology, pp.62-67, 16-18 July 2008, IEEE Xplore.

[228] H.B.Kekre, Tanuja K. Sarode, “Fast Codebook Generation

Algorithm for Color Images using Vector Quantization”

International Journal of Computer Science and Information

Technology (IJCSIT), vol. 1, issue.1, pp.7-12, Jan-June 2008.

[229] J. B. MacQueen, “Some Methods for Classification and

Analysis of Multivariate Observations”, Proceedings of 5-th

Berkeley symposium on Mathematical Statistics and

Probability”, Berkely, University of California Press, vol 1, pp.:

281-297, 1967.

[230] Ghanshyam Parmar,Suman Mitra, “Performance Analysis of

Unsupervised Probabilistic, Possibilistic and Plausibilistic

Clustering Algorithms for Brain Tissue segmentation” IJCA

Special Issue on Computer Aided Soft Computing Techniques

for Imaging and Biomedical Application (CASCT),pp.93-98,

2010.