Contents
Understanding patterns and discovering associations, regularities and anomalies between anatomical structures and normal or abnormal function of the human brain is a fundamental goal in the neuroscience community. Current advances in brain image acquisition techniques have made available enormous amounts of remarkable high-resolution three-dimensional (3-D) image data. The availability of this data has already facilitated many advances in human brain mapping during the last decade. In addition to the continuous development of improved brain imaging techniques, greater computer capabilities and improvements in normalization techniques are leading to the creation of large databases of structure/function information. The analysis and exploitation of such large collections of medical image data still remains a problem though. Major issues in the current attempts for managing this data are the efficiency, effectiveness and robustness of the database and data mining tools used to extract knowledge (in the form of patterns, associations, etc). New tools for content-based (similarity) retrieval, association mining, classifications, etc, can have significant impact in this endeavor. Although considerable progress has been made in content-based image retrieval and classification for general types of images, progress in medical images and in particular in brain images has been very slow since global signatures that are usually employed in the former do not work in the brain imaging domain where the regions of interest occupy a small portion of the image.
The goal of this project is to overcome these problems and address the great need for developing efficient brain data mining tools for the analysis and management of large collections of brain images (from various imaging modalities) and associated clinical data. These automated tools enable interoperable brain image data representation that is easy to search. The main focus is on the management of the spatial regions of interest (ROIs). We are developing a general unified framework for managing ROIs regardless of whether these are lesions, tumors, areas of brain activation, or regions of (normal/abnormal) morphological variability of a variety of brain structures.
The first specific aim focuses on developing efficient methods for feature extraction and classification of ROIs in brain images. The second specific aim focuses on developing fast and effective database techniques supporting efficient retrieval of similar regions of interest in large brain image databases as well as spatial data mining tools for discovering associations between anatomic and other variables such as function, pathology, or response to drugs. Our third specific aim focuses on the integration of the above techniques with morphological analysis tools to correlate morphological changes to changes of other measurements such as functional, physiological, etc. We plan to evaluate and validate the classification, similarity searching and data mining techniques using real and simulated data and to demonstrate their utility in the analysis of large data sets from a number of epidemiological studies of brain morphology and function.
We will demonstrate the utility of the proposed techniques in the analysis of large data sets from a number of epidemiological studies of brain morphology and function including (a) MR spectroscopy and anatomic MRI correlation data sets representing disease states such as multiple sclerosis, stroke, tumors and neurologic disease states, (b) structural MR data on Schizophrenia, (c) structural and functional MR data from normal volunteers and patients with stroke, head trauma, epilepsy, and Alzheimer's disease, (an ongoing study with over 150 participants), (d) structural and functional MR data from a study on aging and (e) Alzheimer's structural MR data from a diverse group of 40 participants.
We are developing novel brain data mining techniques that are efficient, effective and robust. We expect that this work will advance our ability to analyze 3-D brain image data and to discover associations between spatial patterns, anomalies or normal variations and other non-spatial data. Focusing on the ROIs (e.g., lesions, tumors, areas of brain activation, areas of morphological variability) we work on analyzing their spatial characteristics (such as spatial distribution, shape, etc) and on developing effective and efficient methods for characterization, classification, and content-based retrieval of 3-D brain image data, and spatial mining tools for determining associations between structural and functional data obtained using brain imaging techniques and other variables obtained through clinical assessment. Efficiency here is very important; the methods should be scalable so that they can be applied to the analysis of very large data sets coming from multiple studies.
At this stage of development, we have completed an implementation of database tools that allow efficient ROI similarity searches and classification. The similarity queries are handled by extracting features from ROIs, mapping them to a k-dimensional (feature) space and calculating the nearest neighbors of a certain query ROI in feature space. We have also developed preliminary tools for reduction of the dimensionality of this space. Based on these tools we can easily select the most discriminative features making efficient the retrieval of nearest neighbors.
M. Barnathan, V. Megalooikonomou, C. Faloutsos, F. Mohamed, S. Faro, "High-order Concept Discovery in Functional Brain Images", Proceedings of the 7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI), Rotterdam, The Netherlands, 2010 (accepted).
M. Barnathan, V. Megalooikonomou, C. Faloutsos, F.B. Mohamed, S. Faro, "TWave: High-Order Analysis of Spatiotemporal Data", Proceedings of the 14th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Hyderabad, India , 21-24 June, 21-24, 2010 (accepted).
Q. Wang, V. Megalooikonomou, C. Faloutsos, "Time Series Analysis with Multiple Resolutions", Information Systems, Vol. 35, No. 1, pp. 56-74, 2010.
Q. Wang, A. Charisi, L. J. Latecki, J. Gee, V. Megalooikonomou, "Shape Similarity Analysis of Region of Interests in Medical Images", In Proceedings of the SPIE Medical Imaging Conference, San Diego, CA, 2010.
D. Kontos, V. Megalooikonomou, J. Gee, "Morphometric analysis of brain images with reduced number of statistical tests: a study on the gender-related differentiation of the corpus callosum", Artificial Intelligence in Medicine Vol. 47, No. 1, pp. 75-86, 2009.
Q. Wang and V. Megalooikonomou, "A Performance Evaluation Framework for Association Rule Mining in Spatial Data", Intelligent Information Systems, DOI: 10.1007/s10844-009-0115-6, 2009.
L. An, H. Xie, M.H. Chin, Z. Obradovic, D.J. Smith and V. Megalooikonomou, "Analysis of multiplex gene expression maps obtained by voxelation", BMC Bioinformatics, Vol. 10 (Suppl 4): S10, 2009.
L. An, Z. Obradovic, D. Smith, O. Bodenreider and V. Megalooikonomou, "Mining Association Rules among Gene Functions in Clusters of Similar Gene Expression Maps", Proceedings of the Workshop on Data Mining in Functional Genomics, IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Washington D.C., Nov. 2009.
M. Barnathan, R. Li, V. Megalooikonomou, F. Mohamed, S. Faro, "Wavelet Analysis of 4D Motor Task fMRI Data", Proceedings of Computer Assisted Radiology and Surgery (CARS), Barcelona, Spain, 2008.
Q. Wang and V. Megalooikonomou, "A Dimensionality Reduction Technique for Efficient Time Series Similarity Analysis," Information Systems, Vol. 33, No. 1, pp. 115-132, 2008.
L. An, H. Xie, M. Chin, Z. Obradovic, D. Smith, V. Megalooikonomou, "Analysis of Multiplex Gene Expression Maps Obtained By Voxelation", In Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine (BIBM) , Philadelphia, PA, 2008.
M. Barnathan, J. Zhang, E. Miranda, V. Megalooikonomou, S. Faro, H. Hensley, L. D. Valle, K. Khalili, J. Gordon, F. B. Mohamed, "A Texture-Based Methodology For Identifying Tissue Type in Magnetic Resonance Images", Proceedings of the 5th IEEE International Symposium on Biomedical Imaging (ISBI), Paris, France, pp. 464-467, 2008.
M. Barnathan, J. Zhang, V. Megalooikonomou, "A Web-Accessible Framework for the Automated Storage and Texture Analysis of Biomedical Images", Proceedings of the 5th IEEE International Symposium on Biomedical Imaging (ISBI), Paris, France, pp. 257-259, 2008.
Avants, B., Epstein, C., Grossman, M., and Gee, J. C., "Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain", Med Image Anal 12, 1, 26-41, 2008.
Gee, J. C., "Digital brain atlases for biomedicine", IEEE Signal Processing Magazine 25, 1, 138-141, 2008.
Kim, J., Avants, B., Patel, S., Whyte, J., Coslett, B., Pluta, J., Detre, J., and Gee, J. C., "Structural consequences of diffuse traumatic brain injury: a large deformation tensor-based morphometry study", Neuroimage 39, 3, 1014-1026, 2008.
V. Megalooikonomou, D. Kontos, D. Pokrajac, A. Lazarevic and Z. Obradovic, "An adaptive partitioning approach for mining discriminant regions in 3D image data", Journal of Intelligent Information Systems, Vol. 31, No. 3, pp. 217-242, 2008.
Avants B., Duda J., Kim J., Zhang H., Pluta J., Gee J.C., Whyte J., "Multivariate Analysis of Structural and Diffusion Imaging in Traumatic Brain Injury", Academic Radiology, Vol. 15, No. 11, pp: 1360-1375, 2008.
Avants, B., Hurt, H., Giannetta, J., Epstein, C., Shera, D., Rao, H., Wang, J., and Gee, J. C., "Effects of heavy in utero cocaine exposure on adolescent caudate morphology", Pediatr Neurol 37, 4, 275-279, 2007.
Zhang, H., Avants, B., Yushkevich, P., Woo, J., Wang, S., McCluskey, L., Elman, L., Melhem, E., and Gee, J. C., "High-dimensional spatial normalization of diffusion tensor images improves the detection of white matter differences: an example study using amyotrophic lateral sclerosis", IEEE Trans Med Imaging 26, 11, 1585-1597, 2007.
L. Latecki, V. Megalooikonomou, Q. Wang, D. Yu, "An Elastic Partial Shape Matching Technique", Pattern Recognition, Vol. 40, No. 11, pp. 3069-3080, 2007.
V. Megalooikonomou, D. Kontos, "Medical Data Fusion for Telemedicine: A model for distributed analysis of medical image data across clinical information repositories", IEEE Engineering in Medicine and Biology Magazine, Vol. 26, No. 5, pp. 36-42, 2007.
D. Kontos, V. Megalooikonomou, M. Sobel, "A Statistical Approach for Selecting Discriminative Features of Spatial Regions of Interest", Intelligent Data Analysis, Vol. 11, No. 2, pp. 111-135, 2007.
Q. Wang, E. Karamani-Liacouras, E. Miranda, U. S. Kanamalla, V, Megalooikonomou "Classification of brain tumors using MRI and MRS", SPIE Conference on Medical Imaging, 2007.
C. Faloutsos and V. Megalooikonomou, "On Data Mining, Compression, and Kolmogorov Complexity", Data Mining and Knowledge Discovery, Tenth Anniversary Issue, Vol. 15, No. 1, pp. 3-20(18), 2007.
Yushkevich, P.A., Piven, J., Hazlett, H.C., Smith, R.G., Ho, S., Gee, J.C., Gerig, G., "User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability", NeuroImage, 31, pp. 1116-1128, 2006.
Avants, B., Schoenemann, P.T., Gee, J.C., "Lagrangian frame diffeomorphic image registration: Morphometric comparison of human and chimpanzee cortex", Medical Image Analysis, 10, pp. 397-412, 2006.
Q. Wang, V. Megalooikonomou, D. Kontos, E. Miranda, V. Calhoun, "Similarity Searches in Brain Image Databases", Human Brain Mapping Conference (OHBM'06), Florence, Italy, June 11-15, 2006, Neuroimage, Vol. 31, Suppl. 1, pp. S173, 2006.
Q. Wang, V. Megalooikonomou, E. Miranda, E. Karamani-Liacouras, U. S. Kanamalla, "Classification of Brain Tumors in MR Images", Human Brain Mapping Conference (OHBM'06), Florence, Italy, June 11-15, 2006, Neuroimage, Vol. 31, Suppl. 1, pp. S172, 2006.
Zhang, H., Yushkevich, P.A., Alexander, D.C., Gee, J.C., "Deformable registration of diffusion tensor MR images with explicit orientation optimization", Medical Image Analysis, 10, pp. 764-785, 2006.
Avants, B., Grossman, M., Gee, J.C., "Symmetric diffeomorphic image registration: Evaluating automated labeling of elderly and neurodegenerative cortex and frontal lobe", Lecture Notes in Computer Science, Vol. 4057, pp. 50-57, 2006.
L. J. Latecki, V. Megalooikonomou, Q. Wang, R. Lakaemper, C. A. Ratanamahatana, and E. Keogh, "Partial Elastic Matching of Time Series", Proceedings of the Fifth IEEE International Conference on Data Mining (ICDM'05), Houston, Texas, pp. 701-704, Nov. 2005.
D. Pokrajac, V. Megalooikonomou, A. Lazarevic, D. Kontos, Z. Obradovic, "Applying Spatial Distribution Analysis Techniques to Classification of 3D Medical Images", Artificial Intelligence in Medicine, Vol. 33, No. 3, pp. 261-280, 2005.
D. Kontos and V. Megalooikonomou, "Fast and effective characterization for classification and similarity searches of 2D and 3D spatial region data", Pattern Recognition, Vol. 38, No. 11, pp. 1831-1846, 2005.
D. Kontos, V. Megalooikonomou and J. Gee, "Reducing the computational cost for statistical medical image analysis: An MRI study on the sexual morphological differentiation of the corpus callosum", Proceedings of the 18th IEEE International Symposium on Computer-Based Medical Systems (CBMS05), Trinity College, Dublin, Ireland, pp. 282-287, June 23-24, 2005.
V. Megalooikonomou, Q. Wang, G. Li, C. Faloutsos, "A Multiresolution Symbolic Representation of Time Series", Proceedings of the 21st International Conference on Data Engineering (ICDE), Tokyo, Japan, pp. 668-679, 2005.
L. J. Latecki, V. Megalooikonomou, Q. Wang, R. Lakaemper, C. A. Ratanamahatana, E. Keogh, "Partial Matching of Time Series", Proceedings of the 9th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD'05), Porto, Portugal, Lecture Notes in Computer Science, Vol. 3721, pp. 577-584, 2005.
V. Megalooikonomou, D. Kontos, "Integrating clinical information repositories: A framework for distributed analysis of medical image data", Proceedings of the 5th International Network Conference (INC 2005), Special Session on Image, Signal and Distributed Data Processing for Networked eHealth Applications, Samos Island, Greece, pp. 545-552, July 5-7, 2005.
Q. Wang, V. Megalooikonomou, D. Kontos, "A Medical Image Retrieval Framework", Proceedings of the 2005 IEEE International Workshop on Machine Learning for Signal Processing (MLSP05), Mystic, Connecticut, pp. 233-238, Sept. 28-30, 2005.
Q. Wang, V. Megalooikonomou, G. Li, "A Symbolic Representation of Time Series", Proceedings of the 8th IEEE International Symposium on Signal Processing and its Applications (ISSPA05), Sydney, Australia, pp. 28-31, Aug. 28-31, 2005.
D. Kontos, V. Megalooikonomou and J. Gee, "Effective Reduction of Statistical Tests for Morphological Analysis: Application to a Study of the Corpus Callosum", Human Brain Mapping Conference (OHBM'05), Toronto, Canada, June 12-16, 2005.
V. Megalooikonomou, D. Kontos and A. Saykin, "Characterizing 3D Regions of Interest in fMRI Activation Maps", Human Brain Mapping Conference (OHBM'05), Toronto, Canada, June 12-16, 2005.
D. Kontos, V. Megalooikonomou, D. Pokrajac, A. Lazarevic, Z. Obradovic, O. B. Boyko, J. Ford, F. Makedon, A. J. Saykin, "Extraction of Discriminative Functional MRI Activation Patterns and an Application to Alzheimer's Disease", in Lecture Notes in Computer Science, Vol. 3217, Medical Image Computing and Computer-Assisted Intervention. MICCAI 2004: 7th International Conference, Rennes-Saint Malo, France, September 26-29, 2004. Proceedings, Part II, pp. 727-735, 2004.
V. Megalooikonomou, G. Li, Q. Wang, "A Dimensionality Reduction Technique for Efficient Similarity Analysis in Time Series Databases", in Proceedings of the 13th Conference on Information and Knowledge Management (CIKM) 2004 , Washington, DC, pp. 160-161, Nov. 2004.
K. Kumaraswamy, V. Megalooikonomou and C. Faloutsos, "Fractal Dimension and Vector Quantization", Information Processing Letters, Vol. 91, No. 3, pp. 107-113, Aug. 2004.
Q. Wang, D. Kontos, G. Li and V. Megalooikonomou, "Application of Time Series Techniques to Data Mining and Analysis of Spatial Patterns in 3D images", in Proceedings of the International Conference on Acoustics, Speech and Signal Processing, (ICASSP), Montreal, Canada, pp.525-528, 2004.
D. Kontos, V. Megalooikonomou, M. J. Sobel, Q. Wang, "An MCMC Feature Selection Technique for Characterizing and Classifying Spatial Region Data", in Lecture Notes in Computer Science 3138, Proceedings of the International Workshop on Syntactical and Structural Pattern Recognition (SSPR 2004) and Statistical Pattern Recognition (SPR 2004), Lisbon, Portugal, pp. 379-387, 2004.
D. Kontos and V. Megalooikonomou, "Fast and Effective Characterization of 3D Region of Interest in Medical Image Data", in Proceedings of the SPIE International Symposium on Medical Imaging 2004, San Diego, CA, Feb. 2004, Volume 5370 Medical Imaging 2004, pp. 1324-1331.
V. Megalooikonomou, Q. Wang, D. Kontos, G. Li, J. Ford, A. Saykin, "Analysis of Brain Image Data using Sequence Analysis Techniques", Organization for Human Brain Mapping (OHBM'04), Budapest, Hungary, June 13-17, 2004.
D. Kontos, V. Megalooikonomou, Q. Wang, J. Ford, F. Makedon, A. Saykin, "Identifying Discriminative fMRI Activation Signatures in Alzheimer's Disease: Studying a Series of Semantic Decision Tasks", Organization for Human Brain Mapping (OHBM'04), Budapest, Hungary, June 13-17, 2004.
D. Kontos, V. Megalooikonomou, F. Makedon, "Computationally Intelligent Methods for Mining 3D Medical Images", in Lecture Notes in Artificial Intelligence 3025, 3rd Hellenic Conference on Artificial Intelligence, Samos Island, Greece, pp. 72-81, May 2004.
Brain Image Data Management System