Fractal-based brain tumor detection in multimodal MRI

In this work, we investigate the effectiveness of fusing two novel texture features along with intensity in multimodal magnetic resonance (MR) images for pediatric brain tumor segmentation and classification. One of the two texture features involves our Piecewise-Triangular-Prism-Surface-Area (PTPSA) algorithm for fractal feature extraction. The other texture feature exploits our novel fractional Brownian motion (fBm) framework that combines both fractal and wavelet analyses for fractalwavelet feature extraction. We exploit three MR image modalities such as T1 (gadolinium-enhanced), T2 and FLuid-Attenuated Inversion-Recovery (FLAIR), respectively. The extracted features from these multimodality MR images are fused using Self-Organizing Map (SOM). For a total of 204 T1 contrast-enhanced, T2 and FLAIR MR images obtained from nine different pediatric patients, our successful tumor segmentation is 100%. Our experimental results suggest that the fusion of fractal, fractalwavelet and intensity features in multimodality MR images offers better tumor segmentation results when compared to that of just fractal and intensity features in single modality MR images. Next, we exploit a multi-layer feedforward neural network with automated Bayesian regularization to classify the tumor regions from non-tumor regions. The Receiver Operating Characteristic (ROC) curves are obtained to evaluate tumor classification performance. The ROC suggests that at a threshold value of 0.7, the True Positive Fraction (TPF) values range from 75% to 100% for different patients, with the average value of 90%.

[1]  Anders Heijl,et al.  Effects of input data on the performance of a neural network in distinguishing normal and glaucomatous visual fields. , 2005, Investigative ophthalmology & visual science.

[2]  Robert Kozma,et al.  Automated brain data segmentation and pattern recognition using ANN . , 2004 .

[3]  Hamid Soltanian-Zadeh,et al.  Optimal linear transformation for MRI feature extraction , 1996, IEEE Trans. Medical Imaging.

[4]  L O Hall,et al.  Review of MR image segmentation techniques using pattern recognition. , 1993, Medical physics.

[5]  Yan Rolland,et al.  Gender Difference on Magnetic Resonance Imaging Texture Analysis of Human Adipose Tissue , 2001 .

[6]  Bidyut Baran Chaudhuri,et al.  An efficient approach to estimate fractal dimension of textural images , 1992, Pattern Recognit..

[7]  Jayaram K. Udupa,et al.  Interplay between intensity standardization and inhomogeneity correction in MR image processing , 2005, IEEE Transactions on Medical Imaging.

[8]  E Le Rumeur,et al.  Comparison of automated and visual texture analysis in MRI: characterization of normal and diseased skeletal muscle. , 1999, Magnetic resonance imaging.

[9]  Shoji Kido,et al.  Fractal Analysis of Internal and Peripheral Textures of Small Peripheral Bronchogenic Carcinomas in Thin-section Computed Tomography: Comparison of Bronchioloalveolar Cell Carcinomas With Nonbronchioloalveolar Cell Carcinomas , 2003, Journal of computer assisted tomography.

[10]  B. Szabó,et al.  Neural network approach to the segmentation and classification of dynamic magnetic resonance images of the breast: Comparison with empiric and quantitative kinetic parameters1 , 2004 .

[11]  B. Szabó,et al.  Neural network approach to the segmentation and classification of dynamic magnetic resonance images of the breast: comparison with empiric and quantitative kinetic parameters. , 2004, Academic radiology.

[12]  R. Velthuizen,et al.  Brain tumor target volume determination for radiation treatment planning through automated MRI segmentation. , 2004, International journal of radiation oncology, biology, physics.

[13]  L. Schad,et al.  MR image texture analysis--an approach to tissue characterization. , 1993, Magnetic resonance imaging.

[14]  Jerry L. Prince,et al.  Adaptive fuzzy segmentation of magnetic resonance images , 1999, IEEE Transactions on Medical Imaging.

[15]  David J. C. MacKay,et al.  Bayesian Interpolation , 1992, Neural Computation.

[16]  Grégoire Toussaint,et al.  Three dimensional texture analysis in MRI: a preliminary evaluation in gliomas. , 2003, Magnetic resonance imaging.

[17]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Hamid Soltanian-Zadeh,et al.  A comparative analysis of several transformations for enhancement and segmentation of magnetic resonance image scene sequences , 1992, IEEE Trans. Medical Imaging.

[19]  Kei Yamada,et al.  Diagnosis of Alzheimer’s disease using brain perfusion SPECT and MR imaging: which modality achieves better diagnostic accuracy? , 2005, European Journal of Nuclear Medicine and Molecular Imaging.

[20]  Koenraad Van Leemput,et al.  Automated model-based tissue classification of MR images of the brain , 1999, IEEE Transactions on Medical Imaging.

[21]  A Dromigny-Badin,et al.  A pyramidal approach for automatic segmentation of multiple sclerosis lesions in brain MRI. , 1998, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[22]  Yan Zhu,et al.  Computerized tumor boundary detection using a Hopfield neural network , 1997, IEEE Transactions on Medical Imaging.

[23]  Maryellen Giger,et al.  Biomedical Imaging Research Opportunities Workshop: report and recommendations overview of the workshop. , 2003, Academic radiology.

[24]  S Swarnamani,et al.  Application of artificial neural networks for the classification of liver lesions by image texture parameters. , 1996, Ultrasound in medicine & biology.

[25]  Kiralee M. Hayashi,et al.  Abnormal Cortical Complexity and Thickness Profiles Mapped in Williams Syndrome , 2005, The Journal of Neuroscience.

[26]  M. Giger,et al.  Biomedical Imaging Research Opportunities Workshop II: report and recommendations. , 2003, Radiology.

[27]  Paul M. Thompson,et al.  Texture based MRI segmentation with a two-stage hybrid neural classifier , 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).

[28]  B. Mandelbrot,et al.  Fractional Brownian Motions, Fractional Noises and Applications , 1968 .

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

[30]  Robert T. Schultz,et al.  Segmentation and Measurement of the Cortex from 3D MR Images , 1998, MICCAI.

[31]  A. Dale,et al.  High‐resolution intersubject averaging and a coordinate system for the cortical surface , 1999, Human brain mapping.

[32]  Khan M Iftekharuddin,et al.  Statistical analysis of fractal-based brain tumor detection algorithms. , 2005, Magnetic resonance imaging.

[33]  Fernando Flores-Mangas,et al.  Classification of anatomical structures in mr brain images using fuzzy parameters , 2004, IEEE Transactions on Biomedical Engineering.

[34]  C. Sparrow.  The Fractal Geometry of Nature , 1984 .

[35]  J. M. Pastor,et al.  Super-rough dynamics on tumor growth , 1998 .

[36]  Jayaram K. Udupa,et al.  New variants of a method of MRI scale standardization , 2000, IEEE Transactions on Medical Imaging.

[37]  Robert J. Ogg,et al.  Automatic brain tumor detection in MRI: methodology and statistical validation , 2005, SPIE Medical Imaging.

[38]  Ronald Marsh,et al.  Fractal analysis of tumor in brain MR images , 2003, Machine Vision and Applications.

[39]  Thomas K. Pilgram,et al.  Validation of magnetic resonance imaging (MRI) multispectral tissue classification. , 1991, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[40]  Khan M. Iftekharuddin,et al.  Multiresolution-fractal feature extraction and tumor detection: analytical modeling and implementation , 2003, SPIE Optics + Photonics.

[41]  Cornelius T. Leondes.  Analysis and computational methods , 2005 .

[42]  Lawrence O. Hall,et al.  Automatic segmentation of non-enhancing brain tumors in magnetic resonance images , 2001, Artif. Intell. Medicine.

[43]  Guido Gerig,et al.  Automatic brain tumor segmentation by subject specific modification of atlas priors. , 2003, Academic radiology.

[44]  R P Velthuizen,et al.  MRI segmentation: methods and applications. , 1995, Magnetic resonance imaging.

[45]  M. Thelen,et al.  Tissue characterization with T1, T2, and proton density values: results in 160 patients with brain tumors. , 1988, Radiology.

[46]  James C. Bezdek,et al.  A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain , 1992, IEEE Trans. Neural Networks.

[47]  Alan C. Evans,et al.  Automated 3-D Extraction of Inner and Outer Surfaces of Cerebral Cortex from MRI , 2000, NeuroImage.

[48]  D M Doddrell,et al.  A segmentation-based and partial-volume-compensated method for an accurate measurement of lateral ventricular volumes on T(1)-weighted magnetic resonance images. , 2001, Magnetic resonance imaging.

[49]  Teuvo Kohonen,et al.  Self-organization and associative memory: 3rd edition , 1989 .

[50]  Guido Gerig,et al.  A brain tumor segmentation framework based on outlier detection , 2004, Medical Image Anal..

[51]  R. L. Butterfield,et al.  Multispectral analysis of magnetic resonance images. , 1985, Radiology.

[52]  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.

[53]  Dewey Odhner,et al.  A system for brain tumor volume estimation via MR imaging and fuzzy connectedness. , 2005, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.