A Review on Detection of Traumatic brain Injury using Visual-Contextual model in MRI Images

Recently, there are various computational methods to analyze the traumatic brain injury (TBI) from magnetic resonance imaging (MRI).The detection of brain injury is very difficult task in the medical science. There are various soft techniques for the detection of the patch of brain injury on the basis of MRI image contents. This paper gives brief analysis about the different methods to determine the normal and abnormal tissues of the brain

Multimodal MR Prediction Models for Late-Life Depression and Treatment Response

Currently, depression diagnosis relies primarily on behavioral symptoms and signs, instead of underlying brain characteristics, and treatment is guided by trial and error instead of individual suitability associated with underlying brain characteristics. Also, previous brain-imaging studies attempting to resolve this issue have traditionally focused on mid-life depression using a single imaging modality and region-based approach, which may not fully explain the complexity of the underlying brain characteristics; especially for late-life depression. We aimed to evaluate and compare underlying brain characteristics of late-life depression diagnosis and treatment response by estimating accurate prediction models using multi-modal magnetic resonance imaging and non-imaging measures. Based on our finding, late-life depression diagnosis and treatment response predictors involve measures from different imaging modalities, which are indicative of differences in underlying brain characteristics

Analysis Of Brain White Matter Hyperintensities Using Pattern Recognition Techniques

The brain white matter is responsible for the transmission of electrical signals through the central nervous system. Lesions in the brain white matter, called white matter hyperintensity (WMH), can cause a significant functional deficit. WMH are commonly seen in normal aging, but also in a number of neurological and psychiatric disorders. We propose here an automatic method for WHM analysis in order to distinguish regions of interest between normal and non-normal white matter (identification task) and also to distinguish different types of lesions based on their etiology: demyelinating or ischemic (classification task). The method combines texture analysis with the use of classifiers, such as Support Vector Machine (SVM), Nearst Neighboor (1NN), Linear Discriminant Analysis (LDA) and Optimum Path Forest (OPF). Experiments with real brain MRI data showed that the proposed method is suitable to identify and classify the brain lesions. © 2013 SPIE.8669The Society of Photo-Optical Instrumentation Engineers (SPIE),Aeroflex Incorporated,CREOL - Univ. Central Florida, Coll. Opt. Photonics,DQE Instruments, Inc.,Medtronic, Inc.,PIXELTEQ, Multispectral Sensing and ImagingAppenzeller, S., Faria, A.V., Li, L., Costallat, L.T., Cendes, F., Quantitative magnetic resonance imaging analyses and clinical significance of hyperintense white matter lesions in systemic lupus erythematosus patients (2008) Annals of Neurology, 64 (6), pp. 635-643Klöppel, S., Abdulkadir, A., Hadjidemetriou, S., Issleib, S., Frings, L., Thanh, T., Mader, I., Ronneberger, O., A comparison of different automated methods for the detection of white matter lesions in mri data (2011) NeuroImage, 57 (2), pp. 416-422Anbeek, P., Vincken, K.L., Osch, M.J.P., Bisschops, R.H.C., Grond, J., Probabilistic segmentation of whitematter lesions in mr imaging (2004) NeuroImage, 21 (3), pp. 1037-1044Wu, M., Rosano, C., Butters, M., Whyte, E., Nable, M., Crooks, R., Meltzer, C.C., Aizenstein, H.J., A fully automated method for quantifying and localizing white matter hyperintensities on mr images (2006) Psychiatry Research, 148 (2-3), pp. 133-142Zimring, D.G., Achiron, A., Miron, S., Faibel, M., Azhari, H., Automatic detection and characterization of multiple sclerosis lesions in brain mr images (1998) Magnetic Resonance Imaging, 16 (3), pp. 311-318Haralick, R.M., Shanmugam, K., Dinstein, I., Textural features for image classification (1973) , IEEE Transactions on Systems, Man and Cybernetics, 3 (6), pp. 610-621Castellano, G., Bonilha, L., Cendes, F., Texture analysis of medical images (2004) Clinical Radiology, 59 (12), pp. 1061-1069Lerski, R.A., Schad, L., Boyce, D., Blül, S., Zuna, I., Mr image texture analysis: An approach to tissue characterization (1993) Magnetic Resonance Imaging, 11 (6), pp. 873-887Kruggel, F., Paul, J., Gertz, H., Texture-based segmentation of diffuse lesions of the brain's white matter (2008) Neuroimage, 39 (3), pp. 987-996Byun, H., Lee, S.W., Applications of support vector machines for pattern recognition: A survey (2002) Proc. First International Workshop on Pattern Recognition with Support Vector Machines, pp. 213-236Bhatia, N., Survey of nearest neighbor techniques (2010) International Journal of Computer Science and Information Security, 8 (2), pp. 302-305Webb, A.R., (2002) Statistical Pattern Recognition, pp. 123-163. , John Wiley & Sons, MalvernCappabianco, F., Falcão, A., Rocha, L., Clustering by optimum path forest and its application to automatic gm/wm classification in mr-t1 images of the brain (2008) Proc. 5th IEEE International Symposium on Biomedical Imaging: from Nano to Macro, pp. 428-431Lotufo, R., Machado, R., Körbes, A., Ramos, R., Adessowiki: On-line collaborative scientific programming platform (2009) Proc 5th International Symposium on Wikis and Open Collaboration, 10, pp. 1-10. , 6Schwartz, W.R., Siqueira, F.R., Pedrini, H., Evaluation of feature descriptors for texture classification (2012) Journal of Electronic Imaging, 21 (2), pp. 1-17Han, J., Kamber, M., (2006) Data Mining: Concepts and Techniques, pp. 291-310. , Elsevier, San Diego & London & San FransciscoTaylor, J.S., Cristianini, N., (2000) Support Vector Machines and other Kernel-based Learning Methods, pp. 93-122. , Cambridge University Press, New KingdomPapa, J., Falcão, A.X., Suzuki, C.T.N., Supervised pattern classification based on optimum-path forest (2009) International Journal of Imaging Systems and Technology, 19 (2), pp. 120-131Duda, R.O., Hart, P.E., Stork, D.G., (2001) Pattern Classification, , Wiley, Guelph OntarioSouza, R., Rittner, L., Lotufo, R., A comparison between optimum-path forest and k-nearest neighbors classifier (2012) Proc. XXV SIBGRAPI - Conference on Graphics, Patterns and Images, pp. 260-267Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Duchesnay, E., Scikit-learn: Machine learning in python (2011) Journal of Machine Learning Research, 12 (10), pp. 2825-283