کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
528748 | 869604 | 2013 | 17 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Multi-feature gradient vector flow snakes for adaptive segmentation of the ultrasound images of breast cancer Multi-feature gradient vector flow snakes for adaptive segmentation of the ultrasound images of breast cancer](/preview/png/528748.png)
• A new version of generalized gradient flow (GGVF) snakes involving multiple features of the vector field.
• Polynomial diffusion based on prescribed clusters of the edge map.
• A new, directional (orientation) feature integrated into the GGVF snake model.
• Preprocessing based on hierarchical clustering.
• A large series of numerical experiments with an in-depth analysis and tests against recent modifications of GGVF.
Segmentation of ultrasound (US) images of breast cancer is one of the most challenging problems of the modern medical image processing. A number of popular codes for US segmentation are based on a generalized gradient vector flow (GGVF) method proposed by Xu and Prince. The GGVF equations include a smoothing term (diffusion) applied to regions of small gradients of the edge map and a stopping term to fix and extend large gradients appearing at the boundary of the object.The paper proposes two new directions. The first component is diffusion as a polynomial function of the intensity of the edge map. The second component is the orientation score of the vector field. The new features are integrated into the GGVF equations in the smoothing and the stopping term.The algorithms, having been tested by a set of ground truth images, show that the proposed techniques lead to a better convergence and better segmentation accuracy with the reference to conventional GGVF snakes. The adaptive multi-feature snake does not require any hand-tuning. However, it is as efficient as the standard GGVF with the parameters selected by the “brutal force approach”. Finally, proposed approach has been tested against recent modifications of GGVF, i.e. the Poisson gradient vector flow, the mixed noise vector flow and the convolution vector flow. The numerical tests employing 195 synthetic and 48 real ultrasound images show a tangible improvement in the accuracy of segmentation.
Journal: Journal of Visual Communication and Image Representation - Volume 24, Issue 8, November 2013, Pages 1414–1430