Yang Hu

Rochester Institute of Technology, Rochester, New York, United States

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Publications (3)0.2 Total impact

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    ABSTRACT: We propose a framework that efficiently employs intensity, gradient, and textural features for three-dimensional (3-D) segmentation of medical (MRI/CT) volumes. Our methodology commences by determining the magnitude of intensity variations across the input volume using a 3-D gradient detection scheme. The resultant gradient volume is utilized in a dynamic volume growing/formation process that is initiated in voxel locations with small gradient magnitudes and is concluded at sites with large gradient magnitudes, yielding a map comprising an initial set of partitions (or subvolumes). This partition map is combined with an entropy-based texture descriptor along with intensity and gradient attributes in a multivariate analysis-based volume merging procedure that fuses subvolumes with similar characteristics to yield a final/refined segmentation output. Additionally, a semiautomated version of the aforestated algorithm that allows a user to interactively segment a desired subvolume of interest as opposed to the entire volume is also discussed. Our approach was tested on several MRI and CT datasets and the results show favorable performance in comparison to the state-of-the-art ITK-SNAP technique.
    05/2015; 2(2):024003. DOI:10.1117/1.JMI.2.2.024003
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    ABSTRACT: CT (Computed tomography) is a widely employed imaging modality in the medical field. Normally, a volume of CT scans is prescribed by a doctor when a specific region of the body (typically neck to groin) is suspected of being abnormal. The doctors are required to make professional diagnoses based upon the obtained datasets. In this paper, we propose an automatic registration algorithm that helps healthcare personnel to automatically align corresponding scans from 'Study' to 'Atlas'. The proposed algorithm is capable of aligning both 'Atlas' and 'Study' into the same resolution through 3D interpolation. After retrieving the scanned slice volume in the 'Study' and the corresponding volume in the original 'Atlas' dataset, a 3D cross correlation method is used to identify and register various body parts.
    Proceedings of SPIE - The International Society for Optical Engineering 02/2012; DOI:10.1117/12.909322 · 0.20 Impact Factor
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    ABSTRACT: We propose a novel gradient driven methodology for three dimensional (3-D) segmentation of Computed Tomographic (CT) imagery. Our approach begins interactively where-in a user marks a set of voxels within the cross-section of a Sub-Volume Of Interest (SVOI), using a single slice of the CT volume. Subsequently, a 3-D gradient detection scheme is utilized to determine the radiodensity variations across the volume. The resultant gradient information is employed in an iterative volume growing procedure, which is initiated at voxels with small gradient magnitudes adjoining the user-selected voxels and culminates at voxels with large gradient magnitudes, to arrive at the final 3-D segmentation result of the SVOI. The aforementioned method was tested on multiple studies and the results show favorable performance against a state-of-the-art technique.
    18th IEEE International Conference on Image Processing, ICIP 2011, Brussels, Belgium, September 11-14, 2011; 01/2011