Sigmoid gradient vector flow for medical image segmentation

Active contour model has a good performance in consecutive boundary extraction for medical images. The gradient vector flow (GVF) field is one of the most popular external forces that can increase the capture range and converge to concavities, although it is sensitive to image noise and easy to leak in weak edge. Here we propose a novel sigmoid gradient vector flow (SGVF) force model for improving contour performance. This novel external force field is insensitive to noises and may prevent the weak edge leakage. To further illustrate the advantages associated with the proposed GVF field formulation, synthetic images and real images are conducted when the proposed method is applied in ultrasound image and magnetic resonance image for suppressing noise and extracting the weak edges. Experimental results demonstrate that the proposed method leads to more accurate segmentation.

[1]  Jerry L. Prince,et al.  Generalized gradient vector flow external forces for active contours , 1998, Signal Process..

[2]  Jerry L. Prince,et al.  Snakes, shapes, and gradient vector flow , 1998, IEEE Trans. Image Process..

[3]  Junaed Sattar.  Snakes , Shapes and Gradient Vector Flow , 2022 .

[4]  Nikos Paragios,et al.  Gradient vector flow fast geometric active contours , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Bing Li,et al.  Active Contour External Force Using Vector Field Convolution for Image Segmentation , 2007, IEEE Transactions on Image Processing.

[6]  Scott T. Acton,et al.  Motion gradient vector flow: an external force for tracking rolling leukocytes with shape and size constrained active contours , 2004, IEEE Transactions on Medical Imaging.

[7]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[8]  Alan C. Bovik,et al.  Active contours with neighborhood-extending and noise-smoothing gradient vector flow external force , 2012, EURASIP J. Image Video Process..

[9]  Michel Barlaud,et al.  Combining shape prior and statistical features for active contour segmentation , 2004, IEEE Transactions on Circuits and Systems for Video Technology.

[10]  Demetri Terzopoulos,et al.  Topology adaptive deformable surfaces for medical image volume segmentation , 1999, IEEE Transactions on Medical Imaging.

[11]  David A. Clausi,et al.  Tensor vector field based active contours , 2011, 2011 18th IEEE International Conference on Image Processing.

[12]  Demetri Terzopoulos,et al.  A dynamic finite element surface model for segmentation and tracking in multidimensional medical images with application to cardiac 4D image analysis. , 1995, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.