Tag Archives: Medical Image Analysis

Segmentation of biomedical images using shape prior

Screen Shot 2014-05-09 at 10.26.02In this article, a new level set model is proposed for the segmentation of biomedical images. The image energy of the proposed model is derived from a robust image gradient feature which gives the active contour a global representation of the geometric configuration, making it more robust in dealing with image noise, weak edges, and initial configurations. Statistical shape information is incorporated using nonparametric shape density distribution, which allows the shape model to handle relatively large shape variations. The segmentation of various shapes from both synthetic and real images depict the robustness and efficiency of the proposed method.

pdficon_largeS. Y. Yeo, X. Xie, I. Sazonov, and P. Nithiarasu, Segmentation of biomedical images using active contour model with robust image feature and shape prior, International Journal for Numerical Methods in Biomedical Engineering, Volume 30, Issue 2, pages 232–248, February 2014.

More details can be found at the Swansea Vision website.

Integrated Segmentation and Interpolation of Sparse Data

Screen Shot 2014-05-09 at 09.51.59We address the two inherently related problems of segmentation and interpolation of 3D and 4D sparse data and propose a new method to integrate these stages in a level set framework.
The interpolation process uses segmentation information rather than pixel intensities for increased robustness and accuracy. The method supports any spatial configurations of sets of 2D slices having arbitrary positions and orientations. We achieve this by introducing a new level set scheme based on the interpolation of the level set function by radial basis functions. The proposed method is validated quantitatively and/or subjec- tively on artificial data and MRI and CT scans and is compared against the traditional sequential approach, which interpolates the images first, using a state-of-the-art image interpolation method, and then segments the interpolated volume in 3D or 4D. In our experiments, the proposed framework yielded similar segmentation results to the sequential approach but provided a more robust and accurate interpolation. In particular, the interpolation was more satisfactory in cases of large gaps, due to the method taking into account the global shape of the object, and it recovered better topologies at the extremities of the shapes where the objects disappear from the image slices. As a result, the complete integrated framework provided more satisfactory shape reconstructions than the sequential approach.

pdficon_largeA. Paiement, M. Mirmehdi, X. Xie, and M. Hamilton, Integrated Segmentation and Interpolation of Sparse Data, IEEE Transactions on Image Processing (T-IP), volume 23, issue 1, pages 110-125, January 2014.

More details can be found at the Swansea Vision website.

Shape and Appearance Priors for Level Set-based LV Segmentation

We propose a novel spatiotemporal constraint based on shape and appearance and combine it with a level-set deformable model for left ventricle (LV) segmentation in four-dimensional gated cardiac SPECT, particularly in the presence of perfusion defects. The model incorporates appearance and shape information into a ‘soft-to-hard’ probabilistic constraint, and utilises spatiotemporal regularisation via a maximum a posteriori framework. This constraint force allows more flexibility than the rigid forces of shape constraint-only schemes, as well as other state of the art joint shape and appearance constraints. The combined model can hypothesise defective LV borders based on prior knowledge. The authors present comparative results to illustrate the improvement gain. A brief defect detection example is finally presented as an application of the proposed method.

IET Journal of Computer Vision, vol. 7, no.3, pp. 170-183, 2013.

Follow this link to see more publications on Computer Vision and Medical Image Analysis.

Invited Paper: Image Gradient Based Level Set Methods in 2D and 3D

This paper presents an image gradient based approach to perform 2D and 3D deformable model segmentation using level set. The 2D method uses an external force field that is based on magnetostatics and hypothesized magnetic in- teractions between the active contour and object boundaries. The major contribution of the method is that the interaction of its forces can greatly improve the active contour in capturing complex geometries and dealing with difficult initializations, weak edges and broken boundaries. This method is then generalized to 3D by reformulating its external force based on geometrical interactions between the relative geometries of the deformable model and the object boundary characterized by image gradient.

Xianghua Xie et al.,Image Gradient Based Level Set Methods in 2D and 3D, In Deformation Models, Edited by G. Hidalgo et al., Springer, September, 2012.

Follow this link to see more publications on Computer Vision and Medical Image Analysis.

Xianghua Xie joins IET Computer Vision editorial board

Dr. Xianghua Xie was appointed as an Associated Editor of the IET Computer Vision journal. The vision of the journal is to publish the highest quality research work that is relevant and topical to the field, but not forgetting those works that aim to introduce new horizons and set the agenda for future avenues of research in Computer Vision.

New BRU RAs

Warm welcome to Dr. Feng Zhao who joined the group as a post-doc Research Officer under the supervision of Dr. Xianghua Xie. Feng obtained his first degree in a top university in China and PhD from HongKong.

At the same time, Dr. Ben Daubney is now moving to work on the same project that is funded by NISCHR Biomedical Research Unit on Medical Image Analysis and Visualization.

Geometrically Induced Force Interaction for Three-Dimensional Deformable Models

In this paper, we propose a novel 3-D deformable model that is based upon a geometrically induced external force field which can be conveniently generalized to arbitrary dimensions. This external force field is based upon hypothesized interactions between the relative geometries of the deformable model and the object boundary characterized by image gradient. The evolution of the deformable model is solved using the level set method so that topological changes are handled automatically. The relative geometrical configurations between the deformable model and the object boundaries contribute to a dynamic vector force field that changes accordingly as the deformable model evolves. The geometrically induced dynamic interaction force has been shown to greatly improve the deformable model performance in acquiring complex geometries and highly concave boundaries, and it gives the deformable model a high invariancy in initialization configurations. The voxel interactions across the whole image domain provide a global view of the object boundary representation, giving the external force a long attraction range. The bidirectionality of the external force field allows the new deformable model to deal with arbitrary cross-boundary initializations, and facilitates the handling of weak edges and broken boundaries. In addition, we show that by enhancing the geometrical interaction field with a nonlocal edge-preserving algorithm, the new deformable model can effectively overcome image noise. We provide a comparative study on the segmentation of various geometries with different topologies from both synthetic and real images, and show that the proposed method achieves significant improvements against existing image gradient techniques.

S. Yeo, X. Xie, I. Sazonov and P. Nithiarasu,Geometrically Induced Force Interaction for Three-Dimensional Deformable Models, IEEE Transactions on Image Processing (T-IP), volume 20, number 5, pages 1373 – 1387, IEEE CS Press, May 2011.

Follow this link to see more publications on Computer Vision and Medical Image Analysis.