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RISS 인기검색어
- 검색키워드
- 저자 : Mark Green (검색결과 6 건)
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Vessel-Based Fast Deformable Registration with Minimal Strain Energy
Xishi Huang,Jing Ren,Anwar Abdalbari,Mark Green 대한의용생체공학회 2016 Biomedical Engineering Letters (BMEL) Vol.6 No.1
Purpose Image registration for internal organs and softtissues is considered extremely challenging due to organshifts and tissue deformation caused by patients’ movementssuch as respiration and repositioning. In this paper, wepropose a fast deformable image registration method. Thepurpose of our work is to greatly improve the registrationtime while maintaining the registration accuracy. Methods In this study, we formulate the deformable imageregistration problem as a quadratic optimization problem thatminimizes strain energy subject to the constraints of 3Dcurves of blood vessel centerlines and point marks. Theproposed method does not require iteration and is localminimum free. By using 2nd order B-splines to model theblood vessels in the moving image and a new transformationmodel, our method provides a closed-form solution thatimitates the manner in which physical soft tissues deform,thus guarantees a physically consistent match. Results We have demonstrated the effectiveness of ourdeformable technique in registering MR images of the liver. Validation results show that we can achieve a targetregistration error (TRE) of 1.29 mm and an average centerlinedistance error (ACD) of 0.84 ± 0.55 mm. Conclusions This technique has the potential to significantlyimprove registration capabilities and the quality of intraoperativeimage guidance. To the best of our knowledge, thisis the first time that a global analytical solution has beendetermined for the registration energy function with 3Dcurve constraints.
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Automatic error correction using adaptive weighting for vessel-based deformable image registration
Jing Ren,Mark Green,Xishi Huang,Anwar Abdalbari 대한의용생체공학회 2017 Biomedical Engineering Letters (BMEL) Vol.7 No.2
In this paper, we extend our previous work ondeformable image registration to inhomogenous tissues. Inhomogenous tissues include the tissues with embeddedtumors, which is common in clinical applications. It is avery challenging task since the registration method thatworks for homogenous tissues may not work well withinhomogenous tissues. The maximum error normallyoccurs in the regions with tumors and often exceeds theacceptable error threshold. In this paper, we propose a newerror correction method with adaptive weighting to reducethe maximum registration error. Our previous fastdeformable registration method is used in the inner loop. We have also proposed a new evaluation metric averageerror of deformation field (AEDF) to evaluate the registrationaccuracy in regions between vessels and bifurcationpoints. We have validated the proposed method using liverMR images from human subjects. AEDF results show thatthe proposed method can greatly reduce the maximumregistration errors when compared with the previousmethod with no adaptive weighting. The proposed methodhas the potential to be used in clinical applications toreduce registration errors in regions with tumors.
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Seeds Classification for Image Segmentation Based on 3-D Affine Moment Invariants
Anwar Abdalbari,Jing Ren,Mark Green 대한의용생체공학회 2016 Biomedical Engineering Letters (BMEL) Vol.6 No.4
Purpose Image segmentation is a crucial topic in computervision and medical image processing. However, accurateimage segmentation is still a challenging task for many medicalapplications. The region growing based image segmentationprocess starts by selecting seed points within the region ofinterest. Hence, the segmentation algorithm is sensitive to theinitial seeds and the result can be influenced greatly by theaccuracy of seed selection process. Manual seed selectioncan be time-consuming and requires an expert to completethe selection. In this paper, we propose an innovativeapproach to automating the initialization process of the liversegmentation of magnetic resonance images. The seed points,which are needed to initialize the segmentation process weproposed in [1], are extracted and classified by using affineinvariant moments and artificial neural network. Methods We calculated eleven invariant moments for 56different points within the region of interest of an abdominalMR image. These points represent the bifurcation points ofthe vessels centerlines of the liver. In this paper, we dividethe shape of the liver into four regions; left hepatic vein,center hepatic vein, hepatic portal vein, and right hepaticvein. Then, the moments are classified by an artificial neuralnetwork to decide to which part of the liver each point belongs. Results We have validated our proposed technique bycomparing the method with manual seed selection. Theexperimental results show that our method outperforms themanual method in terms of the accuracy of seed pointselection and the speed of the process. Conclusions The proposed technique is considered a robusttechnique for 3D point selection and classification. Theselected seed points are used to initialize the segmentationprocess. The aim of this method is to efficiently detect andidentify the seed points in MR images.
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Preliminary Modelling of Plasco Tower Collapse
Yarlagadda, Tejeswar,Hajiloo, Hamzeh,Jiang, Liming,Green, Mark,Usmani, Asif Council on Tall Building and Urban Habitat Korea 2018 International journal of high-rise buildings Vol.7 No.4
In a recent tragic fire incident, the Plasco Tower collapsed after an intense outburst of fire lasting for three and a half hours and claiming the lives of 16 firefighters and 6 civilians. This paper will present continuing collaborative work between Hong Kong Polytechnic University and Queen's University in Canada to model the progressive collapse of the tower. The fire started at the 10th floor and was observed to have travelled along the floor horizontally and through the staircase and windows vertically. Plasco Tower was steel structure and all the steel sections were fabricated by welding standard European channel or angle profiles and no fire protection was applied. Four internal columns carried the loads transferred by the primary beams, and box columns were constructed along the perimeter of the building as a braced tube for resisting seismic loading. OpenSees fibre-based sections and displacement-based beam-column elements are used to model the frames, while shell elements are used for the reinforced concrete floor slabs. The thermal properties and elevated temperature mechanical properties are as recommended in the Eurocodes. The results in this preliminarily analysis are based on rough estimations of the structure's configuration. The ongoing work looks at modeling the Plasco Tower based on the most accurate findings from reviewing many photographs and collected data.
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