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ABSTRACT: Recent advances in computing hardware have enabled the application of physically based simulation techniques to various research fields for improved accuracy. In this paper, we present a novel physically based non-rigid registration method using smoothed particle hydrodynamics (SPH) for hepatic metastasis volume-preserving registration between follow-up liver CT images. Our method models the liver and hepatic metastasis as a set of particles carrying their own physical properties. Based on the fact that the hepatic metastasis is stiffer than other normal cells in the liver parenchyma, the candidate regions of hepatic metastasis are modeled with particles of higher stiffness compared to the liver parenchyma. Particles placed in the liver and candidate regions of hepatic metastasis in the source image are transformed along a gradient vector flow (GVF)-based force field calculated in the target image. In this transformation, the particles are physically interacted and deformed by a novel deformable particle method which is proposed to preserve the hepatic metastasis to the best. In experimental results using 10 clinical datasets, our method matches the liver effectively between follow-up CT images as well as preserves the volume of hepatic metastasis almost completely, enabling the accurate assessment of the volume change of the hepatic metastasis. These results demonstrated a potential of the proposed method that it can deliver a substantial aid in measuring the size change of index lesion (i.e., hepatic metastasis) after the chemotheraphy of metastasis patients in radiation oncology.
IEEE transactions on bio-medical engineering 04/2013; · 2.15 Impact Factor
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ABSTRACT: In lung cancer screening, benign and malignant nodules can be classified through nodule growth assessment by the registration and, then, subtraction between follow-up computed tomography scans. During the registration, the volume of nodule regions in the floating image should be preserved, whereas the volume of other regions in the floating image should be aligned to that in the reference image. However, ground glass opacity (GGO) nodules are very elusive to automatically segment due to their inhomogeneous interior. In other words, it is difficult to automatically define the volume-preserving regions of GGO nodules. In this paper, we propose an accurate and fast nonrigid registration method. It applies the volume-preserving constraint to candidate regions of GGO nodules, which are automatically detected by gray-level cooccurrence matrix (GLCM) texture analysis. Considering that GGO nodules can be characterized by their inner inhomogeneity and high intensity, we identify the candidate regions of GGO nodules based on the homogeneity values calculated by the GLCM and the intensity values. Furthermore, we accelerate our nonrigid registration by using Compute Unified Device Architecture (CUDA). In the nonrigid registration process, the computationally expensive procedures of the floating-image transformation and the cost-function calculation are accelerated by using CUDA. The experimental results demonstrated that our method almost perfectly preserves the volume of GGO nodules in the floating image as well as effectively aligns the lung between the reference and floating images. Regarding the computational performance, our CUDA-based method delivers about 20× faster registration than the conventional method. Our method can be successfully applied to a GGO nodule follow-up study and can be extended to the volume-preserving registration and subtraction of specific diseases in other organs (e.g., liver cancer).
IEEE Transactions on Biomedical Engineering 11/2011; · 2.28 Impact Factor
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ABSTRACT: This study aimed to introduce heat map, a graphical data presentation method widely used in gene expression experiments, to the presentation and interpretation of image fidelity assessment data of compressed computed tomography (CT) images.
The authors used actual assessment data that consisted of five radiologists' responses to 720 computed tomography images compressed using both Joint Photographic Experts Group 2000 (JPEG2000) 2D and JPEG2000 3D compressions. They additionally created data of two artificial radiologists, which were generated by partly modifying the data from two human radiologists.
For each compression, the entire data set, including the variations among radiologists and among images, could be compacted into a small color-coded grid matrix of the heat map. A difference heat map depicted the advantage of 3D compression over 2D compression. Dendrograms showing hierarchical agglomerative clustering results were added to the heat maps to illustrate the similarities in the data patterns among radiologists and among images. The dendrograms were used to identify two artificial radiologists as outliers, whose data were created by partly modifying the responses of two human radiologists.
The heat map can illustrate a quick visual extract of the overall data as well as the entirety of large complex data in a compact space while visualizing the variations among observers and among images. The heat map with the dendrograms can be used to identify outliers or to classify observers and images based on the degree of similarity in the response patterns.
Medical Physics 08/2011; 38(8):4667-71. · 2.83 Impact Factor
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ABSTRACT: Dental implant surgery, which involves the surgical insertion of a dental implant into the jawbone as an artificial root, has become one of the most successful applications of computed tomography (CT) in dental implantology. For successful implant surgery, it is essential to identify vital anatomic structures such as the inferior alveolar nerve (IAN), which should be avoided during the surgical procedure. Due to the ambiguity of its structure, the IAN is very elusive to extract in dental CT images. As a result, the IAN canal is typically identified in most previous studies. This paper presents a novel method of automatically extracting the IAN canal. Mental and mandibular foramens, which are regarded as the ends of the IAN canal in the mandible, are detected automatically using 3-D panoramic volume rendering (VR) and texture analysis techniques. In the 3-D panoramic VR, novel color shading and compositing methods are proposed to emphasize the foramens and isolate them from other fine structures. Subsequently, the path of the IAN canal is computed using a line-tracking algorithm. Finally, the IAN canal is extracted by expanding the region of the path using a fast marching method with a new speed function exploiting the anatomical information about the canal radius. In experimental results using ten clinical datasets, the proposed method identified the IAN canal accurately, demonstrating that this approach assists dentists substantially during dental implant surgery.
IEEE Transactions on Biomedical Engineering 03/2011; · 2.28 Impact Factor
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ABSTRACT: This study aimed to comparatively evaluate three different image comparison methods: alternate display without an intervening blank image (AWOB), alternate display with an intervening blank image (AWB), and side-by-side display (SSD), in terms of the perceptual sensitivity to image differences between Joint Photographic Experts Group 2000 (JPEG2000) compressed body CT images and their originals.
A total of 50 body CT images obtained with five different scan protocols (5-mm-thick abdomen, 0.67-mm-thick abdomen, 5-mm-thick lung, 0.67-mm-thick lung, and 5-mm-thick low-dose lung) were compressed to one of five compression ratios (reversible, 6:1, 8:1, 10:1, and 15:1) using JPEG2000 algorithm. The fidelity of the compressed images was visually assessed on a four-grade scale independently by five radiologists using each of the three image comparison methods of AWOB, AWB, and SSD. The fidelity grading results for the 40 irreversibly compressed images were compared between the three image comparison methods using the Friedman tests with post hoc Tukey tests. The number of image pairs with no perceptible difference was compared using the exact tests for paired proportions. The time required for the fidelity assessment for all of the 50 compressed images was also compared using the Friedman tests with post hoc Tukey tests.
For the 40 irreversibly compressed images, the fidelity grade was significantly lower for AWOB than for AWB or SSD (p < 0.01 for all readers); however, there was no significant difference between AWB and SSD (p-value range, 0.06-0.92). The percentage of image pairs with no perceptible difference tended to be smaller for AWOB than for AWB (p < 0.01 for all readers) or SSD (p < 0.01 for readers 1-3, p = 0.04 for reader 4, and p = 0.23 for reader 5). However, there was no significant difference between AWB and SSD (p-value range, 0.12- >0.99). For all of the 50 compressed images, the fidelity grading time significantly increased in the order of AWOB, SSD, and AWB.
In assessing the image fidelity of JPEG2000 compressed body CT images, AWOB yields lower fidelity grade and requires less fidelity grading time than AWB or SSD, indicating that AWOB is most sensitive to image differences among of them.
Medical Physics 02/2011; 38(2):836-44. · 2.83 Impact Factor
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ABSTRACT: This study aimed to assess the advantage of the Joint Photographic Experts Group 2000 (JPEG2000) 3D (part 2) over JPEG2000 in compressing abdomen computed tomography (CT) image data sets of different section thicknesses (STs).
Twenty CT scans were reconstructed with six STs (0.67, 1, 2, 3, 4, and 5 mm) and were then compressed to seven compression ratios (CRs) (reversible, 6:1, 8:1, 10:1, 12:1, 14:1, and 16:1) using JPEG2000 and JPEG2000 3D algorithms. Computing (encoding and decoding) times were measured. The image fidelity of the compressed images was quantitatively measured with two computerized image fidelity metrics, peak signal-to-noise ratio (PSNR) and multiscale structural similarity (MS-SSIM). For 120 selected case-relevant images (20 patients x one image per patient x 6 STs), five radiologists independently compared original and compressed images and assessed the fidelity of the compressed images on a four-grade scale. Wilcoxon signed-rank tests and Friedman tests with post hoc Dunn tests were used for the comparisons between the two compressions and among the six STs, respectively
For each combination of the ST and irreversible CR, JPEG2000 3D showed higher image fidelity than JPEG2000 in terms of PSNR (p < 0.0001), MS-SSIM (p < 0.0001), and five radiologists' grading (p-values ranged from <0.0001 to 0.003). At each CR, the advantage of JPEG2000 3D in image fidelity, measured as the differences in the two computerized image fidelity metrics (PSNR and MS-SSIM), significantly increased as the ST increased from 0.67 to 2 mm, and then slowly decreased as the ST increased from 2 to 5 mm. Similar trends were observed in visual analyses of 120 selected images by five radiologists. At each CR, the 3D-to-2D encoding-time ratio significantly decreased (p < 0.001) as the ST increased from 0.67 to 2 mm, and then slowly increased (p < 0.001) as the ST increased from 2 to 5 mm. The 3D-to-2D decoding-time ratio at each CR did not show a notable biphasic trend across the ST.
In compressing abdomen CT image data sets of different STs, the advantage of JPEG2000 3D over JPEG2000 increases as the ST increases from 0.67 to 2 mm, and then slowly decreases as the ST increases from 2 to 5 mm. The practical advantage of JPEG2000 3D is limited for a submillimeter ST due to its greater computing time with only a marginal improvement in image fidelity.
Medical Physics 08/2010; 37(8):4238-48. · 2.83 Impact Factor
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ABSTRACT: This article proposes an accurate and fast deformable registration method between end-exhale and end-inhale CT scans that can handle large lung deformations and accelerate the registration process.
The density correction method is applied to reduce the density difference between two CT scans due to respiration and gravity. The lungs are globally aligned by affine registration and nonlinearly deformed by a demons algorithm using a combined gradient force and active cells. The use of combined gradient force allows a fast convergence in the lung regions with a weak gradient of the target image by taking into account the gradient of the source image. The use of active cells helps to accelerate the registration process and reduce the degree of deformation folding because it avoids unnecessary computation of the displacement for well-matched lung regions.
The proposed method was tested with end-exhale and end-inhale CT scans acquired from eight normal subjects. The performance of the proposed method was evaluated through comparisons of methods that use a target gradient force or a combined gradient force, as well as methods with and without active cells. The proposed method with combined gradient force led to significantly higher accuracy compared to the method with target gradient force. For the entire lung, the proposed method provided a mean landmark error of 2.8 +/- 1.5 mm. For the lower 30% part of the lungs, the Dice similarity coefficient and normalized cross correlation of the proposed method were higher than the original demon algorithm by 2.3% (p=0.0172) and 2.2% (p=0.0028), respectively. The proposed method with an active cell led to fewer voxels with negative Jacobian values and a 55% decrease of processing time compared to the method without an active cell.
The results show that the proposed method can accurately register lungs with large deformations and can considerably reduce the processing time. The proposed deformable registration technique can be used for quantitative assessments of air trapping in obstructive lung disease and for tumor motion tracking during the planning of radiotherapy treatments.
Medical Physics 08/2010; 37(8):4307-17. · 2.83 Impact Factor
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ABSTRACT: This paper presents a fast and accurate marker-based automatic registration technique for aligning uncalibrated projections taken from a transmission electron microscope (TEM) with different tilt angles and orientations. Most of the existing TEM image alignment methods estimate the similarity between images using the projection model with least-squares metric and guess alignment parameters by computationally expensive nonlinear optimization schemes. Approaches based on the least-squares metric which is sensitive to outliers may cause misalignment since automatic tracking methods, though reliable, can produce a few incorrect trajectories due to a large number of marker points. To decrease the influence of outliers, we propose a robust similarity measure using the projection model with a Gaussian weighting function. This function is very effective in suppressing outliers that are far from correct trajectories and thus provides a more robust metric. In addition, we suggest a fast search strategy based on the non-gradient Powell's multidimensional optimization scheme to speed up optimization as only meaningful parameters are considered during iterative projection model estimation. Experimental results show that our method brings more accurate alignment with less computational cost compared to conventional automatic alignment methods.
Physics in Medicine and Biology 06/2010; 55(12):3417-40. · 2.83 Impact Factor
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Proceedings of the 28th International Conference on Human Factors in Computing Systems, CHI 2010, Atlanta, Georgia, USA, April 10-15, 2010; 01/2010
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IEEE Trans. Vis. Comput. Graph. 01/2010; 16:1525-1532.
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ABSTRACT: This paper presents a fast hybrid CPU- and GPU-based CT reconstruction algorithm to reduce the amount of back-projection operation using air skipping involving polygon clipping. The algorithm easily and rapidly selects air areas that have significantly higher contrast in each projection image by applying K-means clustering method on CPU, and then generates boundary tables for verifying valid region using segmented air areas. Based on these boundary tables of each projection image, clipped polygon that indicates active region when back-projection operation is performed on GPU is determined on each volume slice. This polygon clipping process makes it possible to use smaller number of voxels to be back-projected, which leads to a faster GPU-based reconstruction method. This approach has been applied to a clinical data set and Shepp-Logan phantom data sets having various ratio of air region for quantitative and qualitative comparison and analysis of our and conventional GPU-based reconstruction methods. The algorithm has been proved to reduce computational time to half without losing any diagnostic information, compared to conventional GPU-based approaches.
Journal of X-Ray Science and Technology 01/2010; 18(3):221-34. · 1.11 Impact Factor
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ABSTRACT: This paper presents an efficient hybrid registration method using a shell volume that consists of high contrast voxels for
combining PET and high resolution MR (HR-MR) brain images. This approach automatically selects a brain shell volume from the
PET image, and then transforms only the voxels in the brain shell volume into the coordinate space of HR-MR images. Based
on the corresponding voxels in HR-MR images, it finally calculates the best-matching voxel positions using normalized mutual
information (NMI). The shell volume reduces the computation time by using smaller number of corresponding voxels to be matched,
and it even enables a more robust registration. Experimental results on clinical data sets showed that our method successfully
aligned all PET and HR-MR image pairs without losing any diagnostic information, while the conventional NMI method failed
to align some cases.
12/2009: pages 2326-2329;
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ABSTRACT: A robust and fast hybrid method using a shell volume that consists of high contrast voxels with their neighbors is proposed for registering PET and MR/CT brain images. Whereas conventional hybrid methods find the best matched pairs from several manually selected or automatically extracted local regions, our method automatically selects a shell volume in the PET image, and finds the best matched corresponding volume using normalized mutual information (NMI) in overlapping volumes while transforming the shell volume into an MR or CT image. A shell volume not only can reduce irrelevant corresponding voxels between two images during optimization of transformation parameters, but also brings a more robust registration with less computational cost. Experimental results on clinical data sets showed that our method successfully aligned all PET and MR/CT image pairs without losing any diagnostic information, while the conventional registration methods failed in some cases.
Computers in biology and medicine 09/2009; 39(11):961-77. · 1.27 Impact Factor
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ABSTRACT: Recent advances in graphics processing unit (GPU) have enabled direct volume rendering at interactive rates. However, although perspective volume rendering for opaque isosurface is rapidly performed using conventional GPU-based method, perspective volume rendering for non-opaque volume such as translucency rendering is still slow. In this paper, we propose an efficient GPU-based acceleration technique of fast perspective volume ray casting for translucency rendering in computed tomography (CT) colonography. The empty space searching step is separated from the shading and compositing steps, and they are divided into separate processing passes in the GPU. Using this multi-pass acceleration, empty space leaping is performed exactly at the voxel level rather than at the block level, so that the efficiency of empty space leaping is maximized for colon data set, which has many curved or narrow regions. In addition, the numbers of shading and compositing steps are fixed, and additional empty space leapings between colon walls are performed to increase computational efficiency further near the haustral folds. Experiments were performed to illustrate the efficiency of the proposed scheme compared with the conventional GPU-based method, which has been known to be the fastest algorithm. The experimental results showed that the rendering speed of our method was 7.72fps for translucency rendering of 1024x1024 colonoscopy image, which was about 3.54 times faster than that of the conventional method. Since our method performed the fully optimized empty space leaping for any kind of colon inner shapes, the frame-rate variations of our method were about two times smaller than that of the conventional method to guarantee smooth navigation. The proposed method could be successfully applied to help diagnose colon cancer using translucency rendering in virtual colonoscopy.
Computers in biology and medicine 07/2009; 39(8):657-66. · 1.27 Impact Factor
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ABSTRACT: We propose a new method for correcting the segmented lung boundary in expiratory and inspiratory CT. First, the initial lung boundary is extracted by using density-based segmentation. Second, the scope for the boundary propagation is computed by generating and analyzing the gradient profiles with an adaptive length. The definition of the scope helps to prevent the leakage outside the scope and improves the efficiency of the propagation. Finally, the boundary is propagated within the defined scope using a speed function. The speed function is based on the gradient and intensity distribution and prevents the boundary from converging to the local gradient maxima. The results of the lung boundary correction are evaluated by visual inspections, accuracy evaluations and processing time. Experimental results show that the proposed method corrects the lung boundary reliably and reproducibly.
Computers in biology and medicine 03/2009; 39(3):239-50. · 1.27 Impact Factor
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International Conference on Bioinformatics & Computational Biology, BIOCOMP 2009, July 13-16, 2009, Las Vegas Nevada, USA, 2 Volumes; 01/2009
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ABSTRACT: Feature-based registration is an effective technique for clinical use, because it can greatly reduce computational costs. However, this technique, which estimates the transformation by using feature points extracted from two images may cause misalignments, particularly in brain PET and CT images that have low correspondence rates between features due to differences in image characteristics. To cope with this limitation, we propose a robust feature-based registration technique using a Gaussian-weighted distance map (GWDM) that finds the best alignment of feature points even when features of two images are mismatched. A GWDM is generated by propagating the value of the Gaussian-weighted mask from feature points of CT images and leads the feature points of PET images to be aligned on an optimal location even though there is a localization error between feature points extracted from PET and CT images. Feature points are extracted from two images by our automatic brain segmentation method. In our experiments, simulated and clinical data sets were used to compare our method with conventional methods such as normalized mutual information (NMI)-based registration and chamfer matching in accuracy, robustness, and computational time. Experimental results showed that our method aligned the images robustly even in cases where conventional methods failed to find optimal locations. In addition, the accuracy of our method was comparable to that of the NMI-based registration method.
Computers in Biology and Medicine 10/2008; 38(9):945-61. · 1.09 Impact Factor
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ABSTRACT: We propose a fast path planning algorithm using multi-resolution path tree propagation and farthest visible point. Initial path points are robustly generated by propagating the path tree, and all internal voxels locally most distant from the colon boundary are connected. The multi-resolution scheme is adopted to increase computational efficiency. Control points representing the navigational path are successively selected from the initial path points by using the farthest visible point. The position of the initial path point in a down-sampled volume is accurately adjusted in the original volume. Using the farthest visible point, the number of control points is adaptively changed according to the curvature of the colon shape so that more control points are assigned to highly curved regions. Furthermore, a smoothing step is unnecessary since our method generates a set of control points to be interpolated with the cubic spline interpolation. We applied our method to 10 computed tomography datasets. Experimental results showed that the path was generated much faster than using conventional methods without sacrificing accuracy, and clinical efficiency. The average processing time was approximately 1s when down-sampling by a factor of 2, 3, or 4. We concluded that our method is useful in diagnosing colon cancer using virtual colonoscopy.
Computers in Biology and Medicine 10/2008; 38(9):1012-23. · 1.09 Impact Factor
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J. Digital Imaging. 01/2008; 21:306-311.
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ABSTRACT: The pre-integrated volume rendering technique is widely used for creating
high quality images. It produces good images even though the transfer
function is nonlinear. Because the size of the pre-integration lookup
table is proportional to the square of data precision, the required
storage and computation load steeply increase for rendering of high-precision
volume data. In this paper, we propose a method that approximates
the pre-integration function proportional to the data precision.
Using the arithmetic mean instead of the geometric mean and storing
opacity instead of extinction density, this technique reduces the
size and the update time of the pre-integration lookup table so that
it classifies high-precision volume data interactively. We demonstrate
performance gains for typical renderings of volume datasets.
Graphical Models. 01/2008; 70:125-132.
