Mody University International Journal of Computing and Engineering Research

Open Access Open Access  Restricted Access Subscription Access

Characterization of Brain Tumor Using Haralick Parameter and Tumor Volume Calculation


Affiliations
1 Department of Biomedical Engineering,Mody University of Science and Technology, India
2 Department of Electronics and Communication Engineering, Mody University of Science and Technology, India
 

   Subscribe/Renew Journal


CT images have excellent bonny details with the ease of availability. But due to less contrast and details it is less studied. CT images of 5 tumor identified patient were procured. Then this study is divided into three parts. (1) Characterization of tumor using texture analysis. (2) Segmentation of the tumor and (3) volume calculation of the tumor. Preprocessing is important in order to remove the noise and further analysis of image. It is done via contrast enhancement and using median filter the noise is removed. In order to determine the image characteristic we applied texture analysis including Homogeneity, Correlation, Contrast, and Energy. Paired t-test using SPSS software is applied to find the significance of data of tumorous and non-tumorous image. Segmentation and extraction of tumor is performed via Watershed and Fuzzy c-means algorithms. Both the algorithms were evaluated for correctness and completeness. The watershed shows superiority over fuzzy c-means as it lacks robustness. Lastly, volume of the brain tumor is evaluated using MATLAB ® software and compared with the manual results.

Keywords

CT Image, Pre-processing, Texture Analysis, Segmentation, Watershed, Fuzzy C-Means, Volume Calculation, SPSS.
User
Subscription Login to verify subscription
Notifications
Font Size

  • A.F. Peter and C.F. Nick, “MR image texture analysis applied to the diagnosis and tracking of alzheimer’s disease”, IEEE Transactions On Medical Imaging, vol. 17, no. 3, june 1998.

  • B.K. Samir and S.Debasree, “Brain region extraction and volume calculation” UNIASCIT, Vol 1 (1), 44-48, 2011.

  • B.L. ZHU, J. Xiong, X.L. Tan, “Comprehensive Applying Watershed Algorithm in Segmentation of CT Brain Images” IEEE, 978-1-4244-8625-0, 2011.

  • C.H.F. Vincent, Z. Jia-Yin, K.K.B. James, H. Jing, L. Tuan-Kay, “Nasopharyngeal carcinoma tumor volume measurement”, radiol., 231:914–921, 2004.

  • D.J. Hemanth, D. Selvathi and J. Anitha “Effective Fuzzy Clustering Algorithm for Abnormal MR Brain Image Segmentation”, IEEE International Advance Computing Conference (IACC 2009) Patiala, India, 6-7 March 2009.

  • D.S. Saif & A.B. Ahmed, “Segmentation of tumor tissue in gray med ical images using watershed transformation method”, International Journal of Advancements in Computing Technology , volume 2, Number 4, October 2010.

  • G. Castellano, L. Bonilha, L.M. Li, F. Cendes, “Texture analysis of medical images”, Clinical Radiology, 59, 1061–1069, 2004.

  • G. M. N. R. Gajanayake, R. D. Yapal and B. Hewawithana, “Comparison of standard image segmentation methods for segmentation of brain tumors from 2D MR images”, IEEE, 978-1-4244-4837-1, 2009.

  • H.P. Ng, S.H. Ong, K.W.C. Foong, P.S. Goh, W.L. Nowinski, “Medical image segmentation using k-means clustering and improved watershed algorithm”, IEEE, 1-4244-0069-4, 2006

  • H. Qingmao, Q. Guoyu, A. Aamer, L.N. Wieslaw, “Segmentation of brain from computed tomography head images”, Proceedings of the 2005 IEEE , Engineering in Medicine and Biology 27th Annual Conference, Shanghai, China, September 1-4, 2005.

  • J. Jayashri, Mrs. A.C. Phadke, “Special Issue of IJCCT vol. 2 Issues 2, 3, 4, for International Conference [ICCT-2010], 3rd-5th December 2010.

  • Jung Leng Foo, Go Miyano, Thom Lobe, Eliot Winer, “Three-dimensional segmentation of tumors from CT image data using an adaptive fuzzy system”, Computers in Biology and Medicine, 62, 869 – 878, 2009.

  • J. Zhou, K. L. Chan, V. F. H. Chong, S. M. Krishnan,“Extraction of brain tumor from MR images using one-class support vector machine” IEEE, 0-7803-8740-6, 2005.

  • Kaiping Wei, Bin He, T. Zhang，X. Shen, “A Novel Method for Segmentation of CT Head Images”, IEEE, 1-4244-1120-3.

  • K.K. Jong and P.W. Hyun, “Statistical textural features for detection of microcalcifications in digitized mammograms” IEEE Transactions On Medical Imaging, vol. 18, no. 3, march 1999.

  • K. M. Iftekharuddin, J. Zheng, M. A. Islam, R. J. Ogg, F. Lanningham, “Brain tumor detection in MRI: Technique and statistical validation”, IEEE Xplore, 1-4244-0785-0, 2006.

  • K.R. Michael, K.W.Simon, N. Arya, M.B.Peter, A.J. Ferenc, K.Ron, “Automated segmentation of MR images of brain tumors”, Radiology, 218:586–591, 2001.

  • Kovalev VA, Petrou M, Bondar YS. Texture anisotropy in 3Dimages. IEEE Trans Image Process 1999; 8:346–60.

  • L. Alexandra and S. Frisken, “Soft Segmentation of CT Brain Data”. Tufts University, 2007.

  • L.H. Tong, F.A.F. Mohammad and K. Ryoichi,” Segmentation of CT Brain Images Using K-means and EM Clustering”, IEEE Fifth International Conference on Computer Graphics, Imaging and Visualization, 978-0-7695-3359-9, 2008.

  • L. Tianhu and S. Wilfred, “ Statistical approach to X- ray CT Imaging and its applications in image analysis – part II: A new stochastic model – based image segmentation technique for X-ray CT Image”, IEEE Transactions On Medical Imaging, vol. 11, no.1, 1992.

  • M.A. Peter, A.L. Abbott, “Real-Time Image Processing on a Custom Computing platform”, IEEE, 0018-9162, 1995.

  • M.H. Robert, K. Shanmugam, and D. Its'hak, “Textural Features for Image Classification”, IEEE Transactions On Systems, Man, and Cybernetics, November 1973.

  • M. Seyyed, S.H.Mohammad and K.Kamran, “Novel shape-texture feature extraction for medical x-ray image classification”, International Journal of Innovative Computing, Information and Control, Volume 8, Number 1(B), January 2012.

  • NING Chun-yu , LIU Shu-fen. QU Ming, “Research on Removing Noise in Medical Image Based on Median Filter Method” IEEE, 978-1-4244-3930-0, 2009.

  • P.L. Dzung, X. Chenyang, P.L. Jerry, “A survey of current methods in medical image segmentation”, Technical report JHU / ECE, 1999/01.

  • P. Marcel, B.Elizabeth, Sean Ho, G.Gerig, “A brain tumor segmentation framework based on outlier detection”, Medical Image Analysis 8 , 275–283, 2004.

  • P.R. Nikhil and P.K. Sankar, “A review of segmentation techniques”, Pattern Recognition, vol. 26, No. 9, pp. 1277-1294, 1993.

  • R. Ganesan and S. Radhakrishnan, “ Segmentation of computed tomography brain images using genetic algorithm”, International Journal of Computing 4 (4), 157-161: 2009 ISSN: 1816-9503, 2009

  • R.P. Velthuizen, “Validity guided clustering for brain tumor segmentation”, IEEE-EMBC and CMBEC Theme 2 Imaging, 0-7803-2475-7, 1997.

  • R. Rajeev, S. Sanjay, S. K. Sharma, “Brain tumor detection based on multi-parameter MRI image analysis”, ICGST-GVIP Journal, ISSN 1687-398X, Volume (9), Issue (III), June 2009.

  • R. Sudipta, S.Atanu and Prof. B. Samir, “Brain tumor segmentation and quantification from MRI of brain”, journal of global research in computer science, volume 2, No. 4, April 2011. ISSN-2229-371X.

  • S. Karpagam and S. Gowri, “Detection of glioma (tumor) growth by advanced diameter technique using MRI data”, Proceedings of the World Congress on Engineering, vol I, July 6 - 8, 2011.

  • S. Kothainachiar and R. S. D. Wahita Banu, “A supervised watershed algorithm for image segmentation”, Asian Journal of Information Technology 5 (12): 1441-1446, 2006.

  • U.K. Jayaram , L.R.Vicki, S. Hilary, I. Celina, K.S. Punam , G.J. George, Z. Ying, M. Pat, J. Yinpeng, C.M. Leanne, “A Methodology for Evaluating Image Segmentation Algorithms”.

  • W. M. Diyana, W. Zaki, M. Faizal, A. Fauzi, B. Rosli, W. S. H. Munirah, W. Ahmad ,“ Abnormalities detection in serial computed tomography brain images using multi-level segmentation approach”, Multimed Tools Appl , 54:321–340, 2011

  • Yu Jin Zhang, “ A review of recent evaluation methods for image segmentation”. IEEE, 0-7803-6703-0, 2001.


Abstract Views: 348

PDF Views: 122




  • Characterization of Brain Tumor Using Haralick Parameter and Tumor Volume Calculation

Abstract Views: 348  |  PDF Views: 122

Authors

Sandeep Jaiswal
Department of Biomedical Engineering,Mody University of Science and Technology, India
Suneetha Rikhari
Department of Electronics and Communication Engineering, Mody University of Science and Technology, India

Abstract


CT images have excellent bonny details with the ease of availability. But due to less contrast and details it is less studied. CT images of 5 tumor identified patient were procured. Then this study is divided into three parts. (1) Characterization of tumor using texture analysis. (2) Segmentation of the tumor and (3) volume calculation of the tumor. Preprocessing is important in order to remove the noise and further analysis of image. It is done via contrast enhancement and using median filter the noise is removed. In order to determine the image characteristic we applied texture analysis including Homogeneity, Correlation, Contrast, and Energy. Paired t-test using SPSS software is applied to find the significance of data of tumorous and non-tumorous image. Segmentation and extraction of tumor is performed via Watershed and Fuzzy c-means algorithms. Both the algorithms were evaluated for correctness and completeness. The watershed shows superiority over fuzzy c-means as it lacks robustness. Lastly, volume of the brain tumor is evaluated using MATLAB ® software and compared with the manual results.

Keywords


CT Image, Pre-processing, Texture Analysis, Segmentation, Watershed, Fuzzy C-Means, Volume Calculation, SPSS.

References