Object extraction from T2 weighted brain MR image using histogram based gradient calculation

Several segmentation methods have been reported with their own pros and cons. Here we proposed a method for object extraction from T2 weighted (T2) brain magnetic resonance (MR) images. The proposed method is purely based on histogram processing for gradient calculation. The proposed method utilizes the histogram filtering technique as a pre-processing. The primary brain areas; gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) are extracted out efficiently from 2D and 3D images. The method has been successfully implemented on human brain MR images obtained in clinical environment.

[1]  Dmitry P. Vetrov,et al.  Automated atlas-based segmentation of NISSL-stained mouse brain sections using supervised learning , 2011, Programming and Computer Software.

[2]  V. Koshelev,et al.  Research of protective action of ischemic preconditioning on components of blood serum at a brain ischemia by Raman spectroscopy method , 2006 .

[3]  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.

[4]  Jian Fu,et al.  Artificial and biological color: The ABCs , 2007 .

[5]  Natalia Vassilieva.  Content-based image retrieval methods , 2009, Programming and Computer Software.

[6]  Prabhakar R. Gudla,et al.  A high‐throughput system for segmenting nuclei using multiscale techniques , 2008, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[7]  Igor Meglinski,et al.  Application of optical coherence tomography for imaging of scaffold structure and micro-flows characterization , 2008, Canterbury Workshop and School in Optical Coherence Tomography and Adaptive Optics.

[8]  M. Y. Hamza,et al.  Optical properties of normal and thermally coagulated chicken liver tissue measured ex-vivo with diffuse reflectance , 2011 .

[9]  Hamid Soltanian-Zadeh,et al.  Data-guide for brain deformation in surgery: comparison of linear and nonlinear models , 2010, Biomedical engineering online.

[10]  Eric Brassart,et al.  Colour Image Segmentation Using Homogeneity Method and Data Fusion Techniques , 2010, EURASIP J. Adv. Signal Process..

[11]  Jauvane Cavalcante de Oliveira.  Motion Estimation , 2008, Encyclopedia of Multimedia.

[12]  Jerry L Prince,et al.  Current methods in medical image segmentation. , 2000, Annual review of biomedical engineering.

[13]  P. Renshaw,et al.  T2 relaxation time abnormalities in bipolar disorder and schizophrenia , 2010, Magnetic resonance in medicine.

[14]  Ayoub Al-Hamadi,et al.  A new Adaptive Approach for Histogram based Mouth Segmentation , 2009 .

[15]  Bülent Sankur,et al.  Survey over image thresholding techniques and quantitative performance evaluation , 2004, J. Electronic Imaging.

[16]  M. M. Ahmed,et al.  Segmentation of Brain MR Images for Tumor Extraction by Combining Kmeans Clustering and Perona-Malik Anisotropic Diffusion Model , 2008 .

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

[18]  António dos Anjos,et al.  Bi-Level Image Thresholding - A Fast Method , 2008, BIOSIGNALS.

[19]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[20]  Linda G Shapiro,et al.  Head and neck lymph node region delineation with image registration , 2010, Biomedical engineering online.

[21]  Daniel Rueckert,et al.  Segmentation of brain MRI in young children. , 2006, Academic radiology.

[22]  Heiko Neumann,et al.  Motion estimation of magnetic resonance cardiac images using the Wigner-Ville and hough transforms , 2007 .

[23]  Ana Kuzmanić Skelin,et al.  Fast two-step histogram-based image segmentation , 2011 .

[24]  T. R. Gopalakrishnan Nair,et al.  Gradient Based Seeded Region Grow method for CT Angiographic Image Segmentation , 2010, ArXiv.

[25]  Shuichi Makita,et al.  Automatic characterization and segmentation of human skin using three-dimensional optical coherence tomography. , 2006, Optics express.

[26]  Vladimir L. Arlazarov,et al.  Segmentation of small objects in color images , 2008, Programming and Computer Software.

[27]  Hayit Greenspan,et al.  An Adaptive Mean-Shift Framework for MRI Brain Segmentation , 2009, IEEE Transactions on Medical Imaging.

[28]  Max E Valentinuzzi,et al.  Honoring Leslie A. Geddes - Farewell ... , 2010, Biomedical engineering online.

[29]  Adrian Mariampillai,et al.  Can temporal analysis of optical coherence tomography statistics report on dextrorotatory-glucose levels in blood? , 2011 .

[30]  T. W. Ridler,et al.  Picture thresholding using an iterative selection method. , 1978 .

[31]  Asish Ghosh,et al.  Classification of Objects and Background Using Parallel Genetic Algorithm Based Clustering , 2007 .

[32]  Peng Li,et al.  Automated Thickness Measurements of Pearl from Optical Coherence Tomography Images , 2009, 2009 Ninth International Conference on Hybrid Intelligent Systems.

[33]  Christos Davatzikos,et al.  Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme , 2009, Magnetic resonance in medicine.

[34]  Muhammad Attique,et al.  Colorization and Automated Segmentation of Human T2 MR Brain Images for Characterization of Soft Tissues , 2012, PloS one.

[35]  Guang H. Yue,et al.  Automated Histogram-Based Brain Segmentation in T1-Weighted Three-Dimensional Magnetic Resonance Head Images , 2002, NeuroImage.

[36]  Aly A. Farag,et al.  A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data , 2002, IEEE Transactions on Medical Imaging.

[37]  Isabelle Bloch,et al.  Sequential model-based segmentation and recognition of image structures driven by visual features and spatial relations , 2012, Comput. Vis. Image Underst..

[38]  R. L. Green,et al.  Ultrasound of the Eye and Orbit , 1992 .

[39]  Muhammad Atif,et al.  Catheters for optical coherence tomography , 2011 .

[40]  L. Kut.  Ultrasound of the Eye and Orbit , 1994 .

[41]  Neeraj Sharma,et al.  Automated medical image segmentation techniques , 2010, Journal of medical physics.

[42]  C. Kurachi,et al.  Femtosecond light distribution at skin and liver of rats: analysis for use in optical diagnostics , 2010 .

[43]  O. A. Slavin,et al.  Characteristics of Optical Text Recognition Programs , 2004, Programming and Computer Software.

[44]  Mario Vento,et al.  An Enhanced Level Set Algorithm for Wrist Bone Segmentation , 2011 .

[45]  David E. Kleiner,et al.  Liver Regeneration Signature in Hepatitis B Virus (HBV)-Associated Acute Liver Failure Identified by Gene Expression Profiling , 2012, PloS one.

[46]  Erwin Keeve,et al.  3D Markov Random Fields and Region Growing for Interactive Segmentation of MR Data , 2001, MICCAI.

[47]  Alan C. Evans,et al.  A nonparametric method for automatic correction of intensity nonuniformity in MRI data , 1998, IEEE Transactions on Medical Imaging.

[48]  Fuhua Chen,et al.  A soft multiphase segmentation model via Gaussian mixture , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[49]  Jerry L. Prince,et al.  A Survey of Current Methods in Medical Image Segmentation , 1999 .

[50]  Enzo Terreno,et al.  Iopamidol as a responsive MRI‐chemical exchange saturation transfer contrast agent for pH mapping of kidneys: In vivo studies in mice at 7 T , 2011, Magnetic resonance in medicine.

[51]  K. Somasundaram,et al.  Fuzzy c-means technique with histogram based centroid initialization for brain tissue segmentation in MRI of head scans , 2011, 2011 International Symposium on Humanities, Science and Engineering Research.