An unsupervised learning method with a clustering approach for tumor identification and tissue segmentation in magnetic resonance brain images

Novel SOM based FKM algorithm for tissue segmentation and tumor identification in magnetic resonance brain images (T1-w, T2-w, FLAIR and MPR sequences) is proposed through this work.Exact demarcation between tumor and edema region is characterized.Validation of the segmented results by an experienced radiologist.Cross comparison with FCM, SOM, FKM and other hybrid clustering algorithms using ten standard comparison parameters. Malignant and benign types of tumor infiltrated in human brain are diagnosed with the help of an MRI scanner. With the slice images obtained using an MRI scanner, certain image processing techniques are utilized to have a clear anatomy of brain tissues. One such image processing technique is hybrid self-organizing map (SOM) with fuzzy K means (FKM) algorithm, which offers successful identification of tumor and good segmentation of tissue regions present inside the tissues of brain. The proposed algorithm is efficient in terms of Jaccard Index, Dice Overlap Index (DOI), sensitivity, specificity, peak signal to noise ratio (PSNR), mean square error (MSE), computational time and memory requirement. The algorithm proposed through this paper has better data handling capacities and it also performs efficient processing upon the input magnetic resonance (MR) brain images. Automatic detection of tumor region in MR (magnetic resonance) brain images has a high impact in helping the radio surgeons assess the size of the tumor present inside the tissues of brain and it also supports in identifying the exact topographical location of tumor region. The proposed hybrid SOM-FKM algorithm assists the radio surgeon by providing an automated tissue segmentation and tumor identification, thus enhancing radio therapeutic procedures. The efficiency of the proposed technique is verified using the clinical images obtained from four patients, along with the images taken from Harvard Brain Repository.

[1]  Claudio Pollo,et al.  Atlas-based segmentation of pathological MR brain images using a model of lesion growth , 2004, IEEE Transactions on Medical Imaging.

[2]  Stephen Welstead.  Wavelet Image Compression Techniques , 1999 .

[3]  Yi-Fen Tsai,et al.  Automatic MRI Meningioma Segmentation Using Estimation Maximization , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[4]  Alan C. Evans,et al.  Automatic "pipeline" analysis of 3-D MRI data for clinical trials: application to multiple sclerosis , 2002, IEEE Transactions on Medical Imaging.

[5]  Sim Heng Ong,et al.  Segmentation of color images using a two-stage self-organizing network , 2002, Image Vis. Comput..

[6]  Juan Manuel Górriz,et al.  Unsupervised Neural Techniques Applied to MR Brain Image Segmentation , 2012, Adv. Artif. Neural Syst..

[7]  Murugan Pallikonda Rajasekaran,et al.  Segmentation of MR Brain Images for Tumor Extraction Using Fuzzy , 2013 .

[8]  T. Kohonen.  Self-organized formation of topographically correct feature maps , 1982 .

[9]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[10]  Teuvo Kohonen,et al.  Self-organized formation of topologically correct feature maps , 2004, Biological Cybernetics.

[11]  Abdel-Ouahab Boudraa,et al.  Automated segmentation of multiple sclerosis lesions in multispectral MR imaging using fuzzy clustering , 2000, Comput. Biol. Medicine.

[12]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[13]  Jim Z. C. Lai,et al.  A Fuzzy K-means Clustering Algorithm Using Cluster Center Displacement , 2009, J. Inf. Sci. Eng..

[14]  Stephen M. Smith,et al.  Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm , 2001, IEEE Transactions on Medical Imaging.

[15]  Rasoul Khayati,et al.  Fully automated tumor segmentation based on improved fuzzy connectedness algorithm in brain MR images , 2011, Comput. Biol. Medicine.

[16]  Inan Güler,et al.  Interpretation of MR images using self-organizing maps and knowledge-based expert systems , 2009, Digit. Signal Process..

[17]  Sultan Aljahdali,et al.  Improving fuzzy algorithms for automatic image segmentation , 2011, 2011 International Conference on Multimedia Computing and Systems.

[18]  M. V. Velzen,et al.  Self-organizing maps , 2007 .

[19]  El-Hachemi Guerrout,et al.  MEDICAL IMAGE SEGMENTATION ON A CLUSTER OF PCS USING MARKOV RANDOM FIELDS , 2013 .

[20]  S. Murugavalli,et al.  MR Brain Image Segmentation using Bacteria Foraging Optimization Algorithm , 2012 .

[21]  Zhi-Hua Zhou,et al.  SOM Based Image Segmentation , 2003, RSFDGrC.

[22]  W. Eric L. Grimson,et al.  Adaptive Segmentation of MRI Data , 1995, CVRMed.

[23]  P.K Sahoo,et al.  A survey of thresholding techniques , 1988, Comput. Vis. Graph. Image Process..

[24]  D. Tian,et al.  A Brain MR Images Segmentation Method Based on SOM Neural Network , 2007, 2007 1st International Conference on Bioinformatics and Biomedical Engineering.

[25]  Gözde B. Ünal,et al.  Tumor-Cut: Segmentation of Brain Tumors on Contrast Enhanced MR Images for Radiosurgery Applications , 2012, IEEE Transactions on Medical Imaging.

[26]  Jun Xie,et al.  Image segmentation based on maximum-likelihood estimation and optimum entropy-distribution (MLE-OED) , 2004, Pattern Recognit. Lett..

[27]  Farzad Towhidkhah,et al.  Fully automatic segmentation of multiple sclerosis lesions in brain MR FLAIR images using adaptive mixtures method and markov random field model , 2008, Comput. Biol. Medicine.

[28]  Sultan Aljahdali,et al.  Automatic Fuzzy Algorithms for Reliable Image Segmentation , 2012, Int. J. Comput. Their Appl..

[29]  Chen Wei,et al.  Performance evaluation of SVM in image segmentation , 2008, 2008 9th International Conference on Signal Processing.

[30]  Henry Rusinek,et al.  Fully automatic segmentation of the brain from T1‐weighted MRI using Bridge Burner algorithm , 2008, Journal of magnetic resonance imaging : JMRI.

[31]  Yan Li,et al.  MR Brain Image Segmentation Based on Self-Organizing Map Network , 2005 .

[32]  Michael G. Strintzis,et al.  Optimized transmission of JPEG2000 streams over wireless channels , 2006, IEEE Transactions on Image Processing.

[33]  Koenraad Van Leemput,et al.  Automated segmentation of multiple sclerosis lesions by model outlier detection , 2001, IEEE Transactions on Medical Imaging.

[34]  Wei Sun.  Segmentation Method of MRI Using Fuzzy Gaussian Basis Neural Network , 2005 .

[35]  Pallikonda Rajasekaran Murugan,et al.  A complete automated algorithm for segmentation of tissues and identification of tumor region in T1, T2, and FLAIR brain images using optimization and clustering techniques , 2014, Int. J. Imaging Syst. Technol..

[36]  J Jiang,et al.  Medical image analysis with artificial neural networks , 2010, Comput. Medical Imaging Graph..

[37]  Jian Yu,et al.  A Generalized Fuzzy Clustering Regularization Model With Optimality Tests and Model Complexity Analysis , 2007, IEEE Transactions on Fuzzy Systems.

[38]  T. Logeswari,et al.  An improved implementation of brain tumor detection using segmentation based on soft computing , 2010 .

[39]  M. Stella Atkins,et al.  Segmentation of multiple sclerosis lesions in intensity corrected multispectral MRI , 1996, IEEE Trans. Medical Imaging.

[40]  Jorma Laaksonen,et al.  Interactive Retrieval in Facial Image Database Using Self-Organizing Maps , 2005, MVA.

[41]  Nitesh Sinha,et al.  A fully automated algorithm under modified FCM framework for improved brain MR image segmentation. , 2009, Magnetic resonance imaging.

[42]  Javad Alirezaie,et al.  Automatic segmentation of cerebral MR images using artificial neural networks , 1996 .

[43]  Juan Manuel Górriz,et al.  Bayesian Segmentation of Magnetic Resonance Images Using the α-Stable Distribution , 2011, HAIS.

[44]  W. Eric L. Grimson,et al.  Segmentation of brain tissue from magnetic resonance images , 1995, Medical Image Anal..

[45]  Karuppana Gounder Somasundaram,et al.  Fully automatic brain extraction algorithm for axial T2-weighted magnetic resonance images , 2010, Comput. Biol. Medicine.

[46]  Andrés Ortiz,et al.  Segmentation of Brain MRI Using SOM-FCM-Based Method and 3D Statistical Descriptors , 2013, Comput. Math. Methods Medicine.

[47]  Jian Yu,et al.  Optimality test for generalized FCM and its application to parameter selection , 2005, IEEE Transactions on Fuzzy Systems.

[48]  Annette Sterr,et al.  MRI fuzzy segmentation of brain tissue using neighborhood attraction with neural-network optimization , 2005, IEEE Transactions on Information Technology in Biomedicine.

[49]  E. Arsuaga Uriarte,et al.  Topology Preservation in SOM , 2008 .

[50]  S. Welstead.  Fractal and Wavelet Image Compression Techniques , 1999 .

[51]  Jouko Lampinen,et al.  Self-Organizing Maps in data analysis - notes on overfitting and overinterpretation , 2000, ESANN.

[52]  Aly A. Farag,et al.  Modified fuzzy c-mean in medical image segmentation , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).

[53]  P. Vasuda.  Improved Fuzzy C-Means Algorithm for MR Brain Image Segmentation , 2010 .

[54]  Hassan Khotanlou,et al.  Segmentation of Multiple Sclerosis Lesions in Brain MR Images Using Spatially Constrained Possibilistic Fuzzy C-Means Classification , 2011, Journal of medical signals and sensors.

[55]  T. Logeswari,et al.  An Improved Implementation of Brain Tumor Detection Using Segmentation Based on Hierarchical Self Organizing Map , 2010 .

[56]  Juan Manuel Górriz,et al.  Two fully-unsupervised methods for MR brain image segmentation using SOM-based strategies , 2013, Appl. Soft Comput..

[57]  K. Somasundaram,et al.  Automatic brain extraction methods for T1 magnetic resonance images using region labeling and morphological operations , 2011, Comput. Biol. Medicine.

[58]  Suneetha Bobbillapati,et al.  Automatic Detection of Brain Tumor through Magnetic Resonance Image , 2013 .