Texture Analysis for 3D Classification of Brain Tumor Tissues

• Autorzy: Arunadevi B. , Deepa S.

Warianty tytułu

Analiza tekstury w trzywymiarowej klasyfikacji tkanki guza mózgu

• Języki publikacji: EN

Abstrakty

EN

This paper investigates on extending and comparing the Gray level co-occurrence matrices (GLCM) and 3D Gabor filters in volumetric texture analysis of brain tumor tissue classification. The extracted features are sub-selected by genetic algorithm for dimensionality reduction and fed into Extreme Learning Machine Classifier. The organizational prototype of image voxels distinctive to the underlying substrates in a tissue is been evaluated and validated on public and clinical dataset revealing 3D GLCM more appropriate towards brain tumor tissue classification.

PL

W artykule zbadano i porównano algorytmy klasyfikacji tkanki guza mózgu – GLCM i filtry Gabora 3D. Właściwości ekstrakcji były selekcjonowane przy użyciu algorytmu genetycznego i klasyfikatora ELM.

Słowa kluczowe

EN

MRI; brain tumor; Gabor filter; extreme learning machine (ELM)

PL

guz mózgu; ekstrakcja; ELM

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2013
• Tom: R. 89, nr 4
• Strony: 338--342
• Opis fizyczny: Bibliogr. 25 poz., rys., tab.

Twórcy

autor

Arunadevi B.

• Dept. of EEE, Anna University, Regional Office - Coimbatore

autor

Deepa S.

• Dept. of EEE, Anna University, Regional Office - Coimbatore

Bibliografia

• [1] Arunadevi B., Deepa S.N., Brain tumor tissue categorization in 3D magnetic resonance images using improved PSO for extreme learning machine. Progress in Electromagnetics Research B ,49 (2013) , 31-54
• [2] Byun H.,Lee S., Applications of Support Vector Machines for Pattern Recognition: A Survey. Proceedings of Int .Work. Pattern Recognition with Support Vector Machines.Niagara Falls, Canada , (2002), 213-236
• [3] Chris A. Cocosco, Alex P. Zijdenbos, Alan C. Evans., A Fully Automatic and Robust Brain MRI Tissue Classification Method . Medical Image Analysis , 7(2003),No.4, 513 -527
• [4] Chu A., Sehgal C.M., Greenleaf J.F., Use of Gray Value Distribution of Run Lengths for Texture Analysis. Pattern Recognition Letters, 11(1990), No.6 , 415-420
• [5] Carl Philips , Daniel Li, Jacob Furst, Daniela Raicu., An Analysis of Co-Occurrence and Gabor Texture Classification in 2D and 3D .Proceedings of CARS ,( 2008)
• [6] Deepa S.N., Arunadevi B., Second Order Sequential Minimal Optimization for Brain Tumour Classification .European Journal of Scientific Research, 64 (2011), No.3, 377-386
• [7] Dana Cobzas, Neil Birkbeck, Mark Schmidt, Martin Jagersan., 3D Variational Brain Tumor Segmentation using a High Dimensional Feature Set. Proceedings of Int. Conf .Computer Vision (2007), 1-8
• [8] Faraoun K.M., Rabhi A., Data dimensionality reduction based on genetic selection of feature subsets J. Comput. Sci.,6 (2007), 9 -19.
• [9] Gao X., Qian Y.,Hui R., Loomes M., Comley R., Barn B., Chapman A., Rix J., Texture based 3D Image retrieval for medical applications. Proceedings of IADIS International Conference e-Health 2010
• [10] Good C.D., Scahill R.I.,Fox N.T., Automatic differentiation of anatomical patterns in the human brain: Validation with studies of degenerative dementias. Neuroimage,17 (2002), No.1,29-46
• [11] Galloway M.M.,Texture Analysis Using Grey Level Run Lengths. Comp. Graph. And Image Proc, 4 (1975) ,172-179
• [12] Georgiadis P., Cavouras D., Kalatzis I., Glotsos D., Sifaki K., Malamas M., Nikiforidis G., Solomou E., Computer aided discrimination between primary and secondary brain tumors on MRI: From 2D to 3D texture analysis. e-Journal of Science & Technology (e-JST),(2008), 9-18
• [13] Huang G.B., Zhu Q.Y., Siew C.K., Extreme learning machine: Theory and applications. Neurocomputing ,70(2006) ,No.1, 489-501
• [14] Huang G.B., Zhu Q.Y., Siew C.K., Extreme learning machine: a new learning scheme of feedforward neural networks.Proceedings Int .Conf .Neural networks, Budapest, Hungary ,(2004), 985-990
• [15] Haralick R.M., Shanmugam K., Dinstein I.,Textural Features for Image Classification. IEEE Trans. Syst. Man Cybern, 3 (1975), 610-621
• [16] Michael R., Simon K., Nabavi A., Peter M., Ferenc A., Kikinis R.,Automated segmentation of MR images of brain tumors . Radiology, 218 (2001), 586-91
• [17] Mahmoud-Ghoneim D., Toussaint G., Constans J.M., de Certaines J.D.,Three dimensional texture analysis in MRI: a preliminary evaluation in gliomas. Magn Reson Imaging, 21 (2003),983-987
• [18] Mihran Tuceryan, Anil K. Jain, Texture Analysis, Handbook of Pattern Recognition and Computer Vision (2nd Edition), World Scientific Publishing Co., 1998.
• [19] Nan-Ying Liang, Paramasivan Saratchandran, Guang-Bin Huang, Narasimhan Sundararajan,Classification of mental tasks from EEG signals using Extreme Learning Machine. International Journal of Neural Systems,16(2006),No.1, 2938
• [20] Kloppel S., Stonnington C.M., Chu C., Draganski B., Scahill R.I.,Automatic classification of MR scans in Alzheimer's disease. Brain ,131(2008),681-689
• [21] Karin Junker, Dusan Sovilj, Ingrid Kroncke & Joachim W. Dippner, Climate induced changes in benthic macrofauna–a non-linear model approach. Journal of Marine Systems (2012).doi: 10.1016/j.jmarsys.2012.02.005
• [22] Kurani AS, Xu DH, Furst JD and Raicu DS. Co-occurence matrices for volumetric data. In : Proc 7th IASTED Int'l Conf on Computer Graphics and Imaging (2004)
• [23] Tou J.Y., Tay Y.H., Lau P.Y., Recent Trends in Texture Classification: A Review. Proceedings Symposium on Progress, in Information and Communication Technology,(2009), Kuala Lumpur, 63-68
• [24] Zijdenbos A., Forghani R., Evans A., Automatic quantification of MS lesions in 3d MRI brain data sets: Validation of INSECT. Proceedings Int .Conf. Medical Image Computing and Computer Assisted Intervention. Cambridge MA , USA, (1998) 439-448
• [25] Zhu Q.Y., Qin A.K., Suganthan P.N., Huang G.B., Evolutionary extreme learning machine. Pattern Recognition. 38 (2005), No.10 ,1759-1763