With the ever-increasing role of medical images in diagnosis, treatment, and evaluation of treatment effects, extraction of quantitative information from these images and efficient use of the results have become a necessity. In recent years, others and we have developed novel two-dimensional (2D) and three-dimensional (3D) deformable models for a variety of image analysis applications in medicine and industry. We have also developed reliable automated methods for defining the initial shape of the model for segmentation and characterization of hippocampus from magnetic resonance imaging (MRI). These methods need to be extended and feature extraction methods developed to segment and characterize (i.e., determine multi-parametric intensity distribution, texture, shape, surface area, and volume of) brain structures such as hippocampus, amygdala, red nucleus, substantia nigra, globus pallidus, putamen, corpus callosum, and thalamus from MRI. In addition, new databases need to be developed to hold the results with other clinical information (e.g., textual data) in a manner that can be searched, retrieved, and queried conveniently from any computer station. The goal of this project is to develop novel approaches for the above needs. Developments will be done in the context of an important biomedical application and will localize, segment, and characterize hippocampus from MRI. The proposed database will be able to evaluate correlation between a variety of risk factors and post-operative outcomes. The methods will be tested; evaluated, and validated, using simulated images and clinical studies of epileptic patients. Clinical diagnosis based on EEG studies and surgery outcome will be used as """"""""gold standards"""""""" for evaluation and validation of the image analysis methods. The proposed research will be a breakthrough in the development and application of computerized methods for medical image quantitation and object characterization, and will advance image analysis science in the direction of integrating knowledge-based systems, deformable models, texture analysis, and database technology. The proposed approach is applicable to the identification, segmentation, and characterization of other biological structures (e.g., lung, liver, kidneys, cells, neurons). It is also applicable to virtually any image analysis task for which object quantitation and characterization are used.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Research Project (R01)
Project #
5R01EB002450-03
Application #
7069146
Study Section
Special Emphasis Panel (ZRG1-BDCN-E (10))
Program Officer
Cohen, Zohara
Project Start
2004-08-01
Project End
2008-05-31
Budget Start
2006-06-01
Budget End
2007-05-31
Support Year
3
Fiscal Year
2006
Total Cost
$284,747
Indirect Cost
Name
Henry Ford Health System
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
073134603
City
Detroit
State
MI
Country
United States
Zip Code
48202
Fakhraei, Shobeir; Soltanian-Zadeh, Hamid; Fotouhi, Farshad (2014) Bias and Stability of Single Variable Classifiers for Feature Ranking and Selection. Expert Syst Appl 14:6945-6958
Pai, Darshan; Soltanian-Zadeh, Hamid; Hua, Jing (2011) Evaluation of fiber bundles across subjects through brain mapping and registration of diffusion tensor data. Neuroimage 54 Suppl 1:S165-75
Jafari-Khouzani, Kourosh; Elisevich, Kost; Karvelis, Kastytis C et al. (2011) Quantitative multi-compartmental SPECT image analysis for lateralization of temporal lobe epilepsy. Epilepsy Res 95:35-50
Akhondi-Asl, Alireza; Jafari-Khouzani, Kourosh; Elisevich, Kost et al. (2011) Hippocampal volumetry for lateralization of temporal lobe epilepsy: automated versus manual methods. Neuroimage 54 Suppl 1:S218-26
Fakhraei, Shobeir; Soltanian-Zadeh, Hamid; Jafari-Khouzani, Kourosh et al. (2011) Confident Surgical Decision Making in Temporal Lobe Epilepsy by Heterogeneous Classifier Ensembles. IEEE Int Conf Data Min Workshops 2011:1003-1009
Jafari-Khouzani, Kourosh; Elisevich, Kost V; Patel, Suresh et al. (2011) Dataset of magnetic resonance images of nonepileptic subjects and temporal lobe epilepsy patients for validation of hippocampal segmentation techniques. Neuroinformatics 9:335-46
Jafari-Khouzani, Kourosh; Elisevich, Kost; Patel, Suresh et al. (2010) FLAIR signal and texture analysis for lateralizing mesial temporal lobe epilepsy. Neuroimage 49:1559-71
Ghannad-Rezaie, Mostafa; Soltanian-Zadeh, Hamid; Ying, Hao et al. (2010) Selection-Fusion Approach for Classification of Datasets with Missing Values. Pattern Recognit 43:2340-2350
Bijari, Payam B; Akhondi-Asl, Alireza; Soltanian-Zadeh, Hamid (2010) Three-dimensional coupled-object segmentation using symmetry and tissue type information. Comput Med Imaging Graph 34:236-49
Akhondi-Asl, Alireza; Soltanian-Zadeh, Hamid (2010) Two-stage multishape segmentation of brain structures using image intensity, tissue type, and location information. Med Phys 37:4501-16

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