Brain Image Analysis: A Survey

Image analysis is generally a process where digital image processing is utilized to process digital images in order to extract significant statistics or information from the images. The analysis process enables to analyze and visualize medical images of numerous modalities. The paper is basically an overview and discussion of the methods and techniques being proposed and developed in regard of brain image analysis. An image is basically analyzed from the perspective of its segmentation, edge detection, registration and morphology or motion analysis. Here in this paper different brain image types i.e., MRI, CT, PET, EEG/MEG are discussed and presented from the point of operations mentioned above. Each method is discussed and analyzed through its applications, advantages, limitations and results.

[1]  Anthony J. Yezzi,et al.  A geometric snake model for segmentation of medical imagery , 1997, IEEE Transactions on Medical Imaging.

[2]  Léandre Pourcelot,et al.  Multimodal brain images registration with severe pathological information missing , 1999, Proceedings of the First Joint BMES/EMBS Conference. 1999 IEEE Engineering in Medicine and Biology 21st Annual Conference and the 1999 Annual Fall Meeting of the Biomedical Engineering Society (Cat. N.

[3]  Prasun Kumar Roy,et al.  Magnetic resonance image enhancement using stochastic resonance in Fourier domain. , 2010, Magnetic resonance imaging.

[4]  Tsuhan Chen,et al.  Tree-metrics graph cuts for brain MRI segmentation with tree cutting , 2010, 2010 Western New York Image Processing Workshop.

[5]  A. Czerwinska,et al.  Method of segmentation of thorax organs images applied to modelling the cardiac electrical field , 2000, Proceedings of the 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (Cat. No.00CH37143).

[6]  Amit Kumar Mishra,et al.  Detection of Edges in CT Images Directly from Projection Data , 2009, 2009 International Conference on Advances in Recent Technologies in Communication and Computing.

[7]  Jeffrey P. Sutton,et al.  Towards automated enhancement, segmentation and classification of digital brain images using networks of networks , 2001, Inf. Sci..

[8]  Mei Xie,et al.  Automatic Segmentation of Brain CT Image Based on Multiplicate Features and Decision Tree , 2007, 2007 International Conference on Communications, Circuits and Systems.

[9]  Christoph M. Michel,et al.  Towards the utilization of EEG as a brain imaging tool , 2012, NeuroImage.

[10]  Laura Gui,et al.  Automatic segmentation of newborn brain MRI using mathematical morphology , 2011, 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[11]  Wang Wei,et al.  Inner edge detection of PET bottle opening based on the Balloon Snake , 2010, 2010 2nd International Conference on Advanced Computer Control.

[12]  Manuel Desco,et al.  PET and CT image registration of the rat brain and skull using the AIR algorithm , 2000, 2000 IEEE Nuclear Science Symposium. Conference Record (Cat. No.00CH37149).

[13]  Brian B. Avants,et al.  Adaptive graph cuts with tissue priors for brain MRI segmentation , 2006, 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006..

[14]  J. Ronsin,et al.  Region segmentation of CT images using mathematical morphology , 1996, Proceedings of 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[15]  T. Tomitani.  An Edge Detection Algorithm for Attenuation Correction in Emission CT , 1987, IEEE Transactions on Nuclear Science.

[16]  Martin Styner,et al.  A method for frame-by-frame us to CT registration in a joint calibration and registration framework , 2008, 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[17]  Z.Q. Bo,et al.  A novel mathematical morphology based anti-CT saturation criterion for generator differential protection , 2004, 39th International Universities Power Engineering Conference, 2004. UPEC 2004..

[18]  Zhen Zheng,et al.  MRI Head Space-based Segmentation for Object Based Volume Visualization , 2008, 2008 International Conference on Computer Science and Information Technology.

[19]  Jianfeng Xu,et al.  Novel Multistage Three-Dimensional Medical Image Segmentation: Methodology and Validation , 2006, IEEE Transactions on Information Technology in Biomedicine.

[20]  H. Brody.  Medical imaging , 2013, Nature.

[21]  Alireza Behrad,et al.  Automatic spleen segmentation in MRI images using a combined neural network and recursive watershed transform , 2010, 10th Symposium on Neural Network Applications in Electrical Engineering.

[22]  A. Aziz,et al.  Segmentation of brain from computed tomography head images , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[23]  Yi-Xin Zhao,et al.  Crack edge detection of coal CT images based on LS-SVM , 2009, 2009 International Conference on Machine Learning and Cybernetics.

[24]  Kawal S. Rhode,et al.  Model-based respiratory motion correction using 3-D echocardiography , 2008, 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[25]  David Dagan Feng,et al.  Dual-modality 3D brain PET-CT image segmentation based on probabilistic brain atlas and classification fusion , 2010, 2010 IEEE International Conference on Image Processing.

[26]  Jing Bai,et al.  Atlas-Based Fuzzy Connectedness Segmentation and Intensity Nonuniformity Correction Applied to Brain MRI , 2007, IEEE Transactions on Biomedical Engineering.

[27]  Weili Yan,et al.  Research on MRI brain segmentation algorithm with the application in model-based EEG/MEG , 2001 .

[28]  Jyrki Lötjönen,et al.  Evaluation of cardiac PET-MRI registration methods using a numerical breathing phantom , 2004, 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821).

[29]  Liu Jian,et al.  A hybrid method for 3D segmentation of MRI brain images , 2002, 6th International Conference on Signal Processing, 2002..