| 81. |
- Wang, Chunliang, 1980-, et al.
(författare)
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Fast level-set based image segmentation using coherent propagation
- 2014
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Ingår i: Medical physics (Lancaster). - : John Wiley and Sons Ltd. - 0094-2405. ; 41:7
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: The level-set method is known to require long computation time for 3D image segmentation, which limits its usage in clinical workflow. The goal of this study was to develop a fast level-set algorithm based on the coherent propagation method and explore its character using clinical datasets. Methods: The coherent propagation algorithm allows level set functions to converge faster by forcing the contour to move monotonically according to a predicted developing trend. Repeated temporary backwards propagation, caused by noise or numerical errors, is then avoided. It also makes it possible to detect local convergence, so that the parts of the boundary that have reached their final position can be excluded in subsequent iterations, thus reducing computation time. To compensate for the overshoot error, forward and backward coherent propagation is repeated periodically. This can result in fluctuations of great magnitude in parts of the contour. In this paper, a new gradual convergence scheme using a damping factor is proposed to address this problem. The new algorithm is also generalized to non-narrow band cases. Finally, the coherent propagation approach is combined with a new distance-regularized level set, which eliminates the needs of reinitialization of the distance. Results: Compared with the sparse field method implemented in the widely available ITKSnap software, the proposed algorithm is about 10 times faster when used for brain segmentation and about 100 times faster for aorta segmentation. Using a multiresolution approach, the new method achieved 50 times speed-up in liver segmentation. The Dice coefficient between the proposed method and the sparse field method is above 99% in most cases. Conclusions: A generalized coherent propagation algorithm for level set evolution yielded substantial improvement in processing time with both synthetic datasets and medical images.
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| 82. |
- Wang, Chunliang, 1980-, et al.
(författare)
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Fully automatic brain segmentation using model-guided level sets and skeleton-based models
- 2013
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Konferensbidrag (refereegranskat)abstract
- A fully automatic brain segmentation method is presented. First the skull is stripped using a model-based level set on T1-weighted inversion recovery images, then the brain ventricles and basal ganglia are segmented using the same method on T1-weighted images. The central white matter is segmented using a regular level set method but with high curvature regulation. To segment the cortical gray matter, a skeleton-based model is created by extracting the mid-surface of the gray matter from a preliminary segmentation using a threshold-based level set. An implicit model is then built by defining the thickness of the gray matter to be 2.7 mm. This model is incorporated into the level set framework and used to guide a second round more precise segmentation. Preliminary experiments show that the proposed method can provide relatively accurate results compared with the segmentation done by human observers. The processing time is considerably shorter than most conventional automatic brain segmentation methods.
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| 83. |
- Wang, Chunliang, 1980-, et al.
(författare)
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Integrating automatic and interactive method for coronary artery segmentation : let PACS workstation think ahead
- 2010
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Ingår i: International Journal of Computer Assisted Radiology and Surgery. - : Springer Science and Business Media LLC. - 1861-6410 .- 1861-6429. ; 5:3, s. 275-285
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: To provide an efficient method to extract useful information from the increasing amount of coronary CTA.Methods: A quantitative coronary CTA analysis tool was built on OsiriX, which integrates both fully automatic and interactive methods for coronary artery extraction. The computational power of an ordinary PC is exploited by running the non-supervised coronary artery segmentation and centerline tracking in the background as soon as the images are received. When the user opens the data, the software provides a real-time interactive analysis environment.Results: The average overlap between the centerline created in our software and the reference standard was 96.0%. The average distance between them was 0.38 mm. The automatic procedure runs for 3-5 min as a single-thread application in background. Interactive processing takes 3 min in average.Conclusion: In preliminary experiments, the software achieved higher efficiency than the former interactive method, and reasonable accuracy compared to manual vessel extraction.
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| 84. |
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| 85. |
- Wang, Chunliang, et al.
(författare)
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Level-set based vessel segmentation accelerated with periodic monotonic speed function
- 2011
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Ingår i: Medical Imaging 2011. - : SPIE - International Society for Optical Engineering. - 9780819485045 ; , s. 79621M-1-79621M-7
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Konferensbidrag (refereegranskat)abstract
- To accelerate level-set based abdominal aorta segmentation on CTA data, we propose a periodic monotonic speed function, which allows segments of the contour to expand within one period and to shrink in the next period, i.e., coherent propagation. This strategy avoids the contour’s local wiggling behavior which often occurs during the propagating when certain points move faster than the neighbors, as the curvature force will move them backwards even though the whole neighborhood will eventually move forwards. Using coherent propagation, these faster points will, instead, stay in their places waiting for their neighbors to catch up. A period ends when all the expanding/shrinking segments can no longer expand/shrink, which means that they have reached the border of the vessel or stopped by the curvature force. Coherent propagation also allows us to implement a modified narrow band level set algorithm that prevents the endless computation in points that have reached the vessel border. As these points’ expanding/shrinking trend changes just after several iterations, the computation in the remaining iterations of one period can focus on the actually growing parts. Finally, a new convergence detection method is used to permanently stop updating the local level set function when the 0-level set is stationary in a voxel for several periods. The segmentation stops naturally when all points on the contour are stationary. In our preliminary experiments, significant speedup (about 10 times) was achieved on 3D data with almost no loss of segmentation accuracy.
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| 86. |
- Wang, Chunliang, et al.
(författare)
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Making the PACS workstation a browser of image processing software : a feasibility study using inter-process communication techniques
- 2010
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Ingår i: International journal of computer assisted radiology and surgery. - : Springer Science and Business Media LLC. - 1861-6429 .- 1861-6410. ; 5:4, s. 411-419
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Tidskriftsartikel (refereegranskat)abstract
- PURPOSE: To enhance the functional expandability of a picture archiving and communication systems (PACS) workstation and to facilitate the integration of third-part image-processing modules, we propose a browser-server style method. METHODS: In the proposed solution, the PACS workstation shows the front-end user interface defined in an XML file while the image processing software is running in the background as a server. Inter-process communication (IPC) techniques allow an efficient exchange of image data, parameters, and user input between the PACS workstation and stand-alone image-processing software. Using a predefined communication protocol, the PACS workstation developer or image processing software developer does not need detailed information about the other system, but will still be able to achieve seamless integration between the two systems and the IPC procedure is totally transparent to the final user. RESULTS: A browser-server style solution was built between OsiriX (PACS workstation software) and MeVisLab (Image-Processing Software). Ten example image-processing modules were easily added to OsiriX by converting existing MeVisLab image processing networks. Image data transfer using shared memory added <10ms of processing time while the other IPC methods cost 1-5 s in our experiments. CONCLUSION: The browser-server style communication based on IPC techniques is an appealing method that allows PACS workstation developers and image processing software developers to cooperate while focusing on different interests.
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| 87. |
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| 88. |
- Wang, Chunliang, et al.
(författare)
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Model-based left ventricle segmentation in 3D ultrasound using phase image
- 2014
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- In this paper, we propose a semi-automatic method for left ventricle segmentation. The proposed method utilizes a multi-scale quadrature filter method to enhance the 3D volume, followed by a model-based level set method to segment the endocardial surface of the left ventricle. The phase map from the quadrature filters is also used to weight the influence of contour points when updating the statistical model.
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| 89. |
- Wang, Chunliang, 1980-, et al.
(författare)
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Multi-organ Segmentation Using Shape Model Guided Local Phase Analysis
- 2015
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Ingår i: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. - Cham : Springer. - 9783319245744 - 9783319245737 ; , s. 149-156
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Bokkapitel (refereegranskat)abstract
- To improve the accuracy of multi-organ segmentation, we propose a model-based segmentation framework that utilizes the local phase information from paired quadrature filters to delineate the organ boundaries. Conventional local phase analysis based on local orientation has the drawback of outputting the same phases for black-to-white and white-to-black edges. This ambiguity could mislead the segmentation when two organs’ borders are too close. Using the gradient of the signed distance map of a statistical shape model, we could distinguish between these two types of edges and avoid the segmentation region leaking into another organ. In addition, we propose a level-set solution that integrates both the edge-based (represented by local phase) and region-based speed functions. Compared with previously proposed methods, the current method uses local adaptive weighting factors based on the confidence of the phase map (energy of the quadrature filters) instead of a global weighting factor to combine these two forces. In our preliminary studies, the proposed method outperformed conventional methods in terms of accuracy in a number of organ segmentation tasks.
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| 90. |
- Wang, Chunliang, 1980-, et al.
(författare)
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Multiorgan Segmentation Using Coherent Propagating Level Set Method Guided by Hierarchical Shape Priors and Local Phase Information
- 2017
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Ingår i: Cloud-Based Benchmarking of Medical Image Analysis. - Cham : Springer. - 9783319496429 - 9783319496443 ; , s. 165-183
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- In this chapter, we introduce an automatic multiorgan segmentation method using a hierarchical-shape-prior-guided level set method. The hierarchical shape priors are organized according to the anatomical hierarchy of the human body, so that the children structures are always contained by the parent structure. This hierarchical approach solves two challenges of multiorgan segmentation. First, it gradually refines the prediction of the organs’ position by locating and segmenting the larger parent structure. Second, it solves the ambiguity of boundary between two attaching organs by looking at a large scale and imposing the additional shape constraint of the higher-level structures. To improve the segmentation accuracy, a model-guided local phase term is introduced and integrated with the conventional region-based energy function to guide the level set propagation. Finally, a novel coherent propagation method is implemented to speed up the model-based level set segmentation. In the VISCERAL Anatomy challenge, the proposed method delivered promising results on a number of abdominal organs.
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