International Journal of Information Technology and Computer Science(IJITCS)
ISSN: 2074-9007 (Print), ISSN: 2074-9015 (Online)Publisher: MECS
- IJITCS Vol. 5, No. 5, April 2013
Study of Techniques used for Medical Image Segmentation and Computation of Statistical Test for Region Classification of Brain MRI
|Full Text (PDF, 707KB), PP.44-53, DOI: 10.5815/ijitcs.2013.05.06|
|White Matter(WM), Gray Matter(GM), Ceribrospinal Fluid(CSF), Confusion Matrix|
This paper explores the possibility of applying techniques for segmenting the regions of medical image. For this we need to investigate the use of different techniques which helps for detection and classification of image regions. We also discuss some segmentation methods classified by researchers. Region classification is an essential process in the visualization of brain tissues of MRI. Brain image is basically classified into three regions; WM, GM and CSF. The forth region can be called as the tumor region, if the image is not normal. In the paper; Segmentation and characterization of Brain MR image regions using SOM and neuro fuzzy techniques, we integrate Self Organizing Map(SOM) and Neuro Fuzzy scheme to automatically extract WM, GM, CSF and tumor region of brain MRI image tested on three normal and three abnormal brain MRI images. Now in this paper this scheme is further tested on axial view images to classify the regions of brain MRI and compare the results from the Keith‘s database. Using some statistical tests like accuracy, precision, sensitivity, specificity, positive predictive value, negative predictive value, false positive rate, false negative rate, likelihood ratio positive, likelihood ratio negative and prevalence of disease we calculate the effectiveness of the scheme.
Dzung L.Pham et.al. A survey of current methods in medical image segmentation. Annual Review of Biomedical Engineering,January 1998.
Anamika Ahirwar, R.S. Jadon. Segmentation and characterization of Brain MR image regions using SOM and neuro fuzzy techniques. Proceedings of the First International Conference on Emerging Trends in Soft Computing and ICT (SCIT2011), 16-17 March 2011 at Guru Ghasidas Vishwavidyalaya, Bilaspur (C.G.) India, ISBN: 978-81-920913-3-4, pp 128-131.
A.D. Jepson and D.J. Fleet. Image Segmentation. 2007.
Wen-Xiong Kang, Qing-Qiang Yang, Run-Peng Liang. The Comparative Research on Image Segmentation Algorithms.
C. Li, C.Y. Xu, C.F. Gui, and M.D. Fox. Level Set Evolution without Re-initialization: A New Variational Formulation, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
Paresh Chandra Barman et.al. MRI IMAGE SEGMENTATION USING LEVEL SET METHOD AND IMPLEMENT AN MEDICAL DIAGNOSIS SYSTEM. Computer Science & Engineering: An International Journal (CSEIJ), Vol.1, No.5, December 2011.
H.S.Prasantha et.al. MEDICAL IMAGE SEGMENTATION.(IJCSE) International Journal on Computer Science and Engineering. Vol. 02, No. 04, 2010, 1209-1218.
Ajala Funmilola A et. al.,“Fuzzy k-c-means Clustering Algorithm for Medical Image Segmentation”. Journal of Information Engineering and Applications, ISSN 2224-5782 (print) ISSN 2225-0506 (online) Vol 2, No.6, 2012.
S. Murugavalli and V. Rajamani. An Improved Implementation of Brain Tumor Detection Using Segmentation Based on Neuro Fuzzy Technique. Journal of Computer Science 3 (11): 841-846, 2007, ISSN 1549-3636.
Nahla Ibraheem Jabbar, and Monica Mehrotra. Application of Fuzzy Neural Network for Image Tumor Description. Proceedings of world academy of science, engineering and technology volume 34 October 2008 ISSN 2070-3740.
Y. Zhu, Z. Chi and H. Yan. Brain Image Segmentation Using Fuzzy Classifiers.
S. Murugavalli, V. Rajamani. A HIGH SPEED PARALLEL FUZZY C-MEAN ALGORITHM FOR BRAIN TUMOR SEGMENTATION. BIME Journal, Volume (06), Issue (1), Dec. 2006.
Shijuan He et.al. MRI Brain Images Segmentation. IEEE 0-7803-6253-5/00/$10.00©2000.
Yan Li and Zheru Chi. MR Brain Image Segmentation Based on Self-Organizing Map Network. International Journal of Information Technology Vol. 11, No. 8, 2005.
Jianhua Xuan, Tiilay Adali, Yue Wang. SEGMENTATION OF MAGNETIC RESONANCE BRAIN IMAGE: INTEGRATING REGION GROWING AND EDGE DETECTION. IEEE 1995.
Sahoo PK, Soltani S, Wong AKC. A survey of thresholding techniques. Comp Vis Graph Image Proc 1988; 41:233–260.
Singleton HR, Pohost GM. Automatic cardiac MR image segmentation using edge detection by tissue classification in pixel neighborhoods. Magn Reson Med 1997; 37:418–424.
Polakowski WR, Cournoyer DA, Rogers SK, et al. Computer-aided breast cancer detection and diagnosis of masses using difference of Gaussians and derivative-based feature saliency. IEEE Trans Med Imaging 1997; 16:811–819.
Cheng HD, Lui YM, Freimanis RI. A novel approach to microcalcification detection using fuzzy logic technique. IEEE Trans Med Imaging 1998; 17:442–450.
Manousakas N, Undrill PE, Cameron GG, et al. Split-and-merge segmentation of magnetic resonance medical images: performance evaluation and extension to three dimensions. Comp Biomed Res 1998; 31: 393–412.
Udupa K, Samarasekera S. Fuzzy connectedness and object definition: theory, algorithms and applications in image segmentation. Graph Models Image Process 1996; 58:246–261.
Bezdek JC, Hall LO, Clarke LP. Review of MR image segmentation techniques using pattern recognition. Med Phys 1993; 20:1033–1048.
Schalkoff J. Pattern recognition: statistical, structural and neural approach. New York: Wiley & Sons, 1992.
Zijdenbos AP, Dawant BM. Brain segmentation and white matter lesion detection in MR images. Crit Rev Biomed Eng 1994; 22:401–465.
Pham D. L., Xu C., and Prince J. L., A Survey of Current Methods in Medical Image Segmentation. Annual Review of Biomedical Engineering, 1998.
Xu R., and Wunsch D. Survey of Clustering Algorithms. IEEE Transactions on Neural Networks, vol. 16, no. 3, May 2005.
Engr. V. C. Chijindu et.al.,Medical Image Segmentation Methodologies – A Classified Overview, African Journal of Computing & ICT. ISSN 2006-1781, Vol 5. No. 5, Sept 2012.
Hall LO, Bensaid AM, Clarke LP, et al. A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain. IEEE Trans Neural Netw 1992; 3:672–682.
Gelenbe E, Feng Y, Krishnan KRR. Neural network methods for volumetric magnetic resonance imaging of the human brain. Proc IEEE 1996; 84:1488–1496.
Reddick WE, Glass JO, Cook EN, et al. Automated segmentation and classification of multispectral magnetic resonance images of brain using artificial neural networks. IEEE Trans Med Imaging 1997; 16:911–918.
Vilarino DL, Brea VM, Cabello D, et al. Discrete-time CNN for image segmentation by active contours. Pattern Recognit Lett 1998; 19:721–734.
Davatzikos C, Bryan RN. Using a deformable surface model to obtain a shape representation of the cortex. IEEE Trans Med Imaging 1996;15:785–795.
McInerney T, Terzopoulos D. Medical image segmentation using topologically adaptable surfaces. Lect Notes Comp Sci 1997; 1205:23–32.
Xu C, Pham DL, Prince JL, et al. Reconstruction of the central layer of the human cerebral cortex from MR images. In Proc Int Conf Med Image Comp Comp Assist Interv. Cambridge, MA; 1998, pp. 482–488.
Bardinet E, Cohen LD, Ayache N. A parametric deformable model to fit unstructured 3D data. Comp Vis Image Underst 1998; 71:39–54.
Neumann A, Lorenz C. Statistical shape model based segmentation of medical images. Comp Med Image Graph 1998; 22:133–143.
Cohen LD. On active contour models and balloons. CVGIP: Image Underst 1991; 53:211–218.
Caselles V, Catte F, Coll T, et al. A geometric model for active contours. Number Math 1993; 66:1–31.
Xu C, Prince JL. Snakes, shapes, and gradient vector flow. IEEE Trans Image Proc 1998; 7:359–369.
McInerney T, Terzopoulos D. Topologically adaptable snakes. In: Proc Int Conf Comp Vis. Cambridge, MA: IEEE Comp Soc, 1995; 840–845.
McInerney T, Terzopoulos D. Deformable models in medical image analysis: a survey. Med Image Anal 1996; 1:91–108.
Maintz JBM, Viergever MA. A survey of medical image registration. Med Image Anal 1998; 2:1–36.
Collins DL, Holmes CJ, Peters TM. Evans neuroanatomical segmentation. Hum Brain Mapp 1995; 3:190–208.
Aboutanos GB, Dawant BM. Automatic brain segmentation and validation:image-based versus atlas-based de-formable models. SPIE Proc Med Imag 1997; 3034:299–310.
Thompson P, Toga AW. Detection, visualization and animation of abnormal anatomic structure with a probabilistic brain atlas based on random vector field transformations. Med Image Anal 1997; 1:271–294.
Pathak SD, Grimm PD, Chalana V, et al. Pubic arch detection in transrectal ultrasound guided prostate cancer therapy. IEEE Trans Med Imaging 1998; 17:762–771.
Bae KT, Giger ML, Chen C, et al. Automatic segmentation of liver structure in CT images. Med Phys 1993; 20:71–78.
Yang Fei and Jong Won Park. A New Variational Level Set Evolving Algorithm for Image Segmentation.
Anamika Ahirwar,"Study of Techniques used for Medical Image Segmentation and Computation of Statistical Test for Region Classification of Brain MRI", IJITCS, vol.5, no.5, pp.44-53, 2013.DOI: 10.5815/ijitcs.2013.05.06
Copyright © 2007-2017 Modern Education and Computer Science Press. All Rights Reserved.