Dr. Reza Momenan Profile

Dr. Reza Momenan

Staff Scientist at National Institutes of Health

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Publications (3)

Proceedings Article | 21 May 1999 Paper

Hierarchical approach for automated segmentation of the brain volume from MR images

Li-Yueh Hsu, Murray Loew, Reza Momenan

Proceedings Volume 3661, (1999) https://doi.org/10.1117/12.348492

KEYWORDS: Brain, Image segmentation, Magnetic resonance imaging, Neuroimaging, Tissues, Binary data, Head, Quantitative analysis, Image registration, Image processing algorithms and systems

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Image segmentation is considered one of the essential steps in medical image analysis. Cases such as classification of tissue structures for quantitative analysis, reconstruction of anatomical volumes for visualization, and registration of multi-modality images for complementary study often require the segmentation of the brain to accomplish the task. In many clinical applications, parts of this task are performed either manually or interactively. Not only is this proces often tedious and time-consuming, it introduces additional external factors of inter- and intra-rater variability. In this paper, we present a 3D automated algorithm for segmenting the brain from various MR images. This algorithm consists of a sequence of pre-determined steps: First, an intensity window for initial separation of the brain volume from the background and non-brain structures is selected by using probability curves fitting on the intensity histogram. Next, a 3D isotropic volume is interpolated and an optimal threshold value is determined to construct a binary brain mask. The morphological and connectivity processes are then applied on this 3D mask for eliminating the non-brain structures. Finally, a surface extraction kernel is applied to extract the 3D brain surface. Preliminary results from the same subjects with different pulse sequences are compared with the manual segmentation. The automatically segmented brain volumes are compared with the manual results using the correlation coefficient and percentage overlay. Then the automatically detected surfaces are measured with the manual contouring in terms of RMS distance. The introduced automatic segmentation algorithm is effective on different sequences of MR data sets without any parameter tuning. It requires no user interaction so variability introduced by manual tracing or interactive thresholding can be eliminated. Currently, the introduced segmentation algorithm is applied in the automated inter- and intra-modality image registration. It will furthermore be used in different applications such as quantitative analysis of normal and abnormal brain tissues.

Proceedings Article | 8 July 1994 Paper

Study of statistical methods applied in the spatial, wavelet and Fourier domain to enhance and analyze group characteristics of images: application to positron emission tomography brain images

Daniel Rio, Robert Rawlings, Urs Ruttimann, Reza Momenan

Proceedings Volume 2299, (1994) https://doi.org/10.1117/12.179250

KEYWORDS: Wavelets, Statistical analysis, Positron emission tomography, Brain, Statistical methods, Neuroimaging, Image resolution, Image classification, Image enhancement, Spatial resolution

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Proceedings Article | 1 July 1990 Paper

Application of statistical pattern recognition and image processing in ultrasound tissue characterization

Reza Momenan, Murray Loew, Robert Wagner, Brian Garra, Michael Insana

Proceedings Volume 1233, (1990) https://doi.org/10.1117/12.18906

KEYWORDS: Image processing, Ultrasonography, Tissues, Image segmentation, Image processing algorithms and systems, Liver, Kidney, Medical imaging, Tumors, Visualization

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