Active contours driven by global and local weighted signed pressure force for image segmentation

Abstract This paper proposes a new global and local weighted signed pressure force (SPF) based active contour model (ACM) to segment various types of images. First, by introducing the normalized global minimum absolute differences as the coefficients of global inner and outer region fitting centers, a new global weighted SPF (GWSPF) is defined, which makes the best of the difference information of inner and outer regions and improves segmentation performance. Second, by introducing the normalized local minimum absolute differences as the coefficients of local inner and outer region fitting centers similarly, a new local weighted SPF (LWSPF) is defined and added to the above global weighted SPF. Third, the global and local within-class variances of the image are used to weight the GWSPF and the LWSPF, which can automatically adjust the effect degrees of the GWSPF and the LWSPF. Experiments on many kinds of real-world images have validated that the proposed model is superior to popular ACMs in segmentation accuracy, in addition, it is robust to the initial curve.

[1]  Yiquan Wu,et al.  Active contours driven by novel LGIF energies for image segmentation , 2017 .

[2]  Michael Unser,et al.  Snakes on a Plane: A perfect snap for bioimage analysis , 2015, IEEE Signal Processing Magazine.

[3]  Lei Zhang,et al.  Active contours driven by local image fitting energy , 2010, Pattern Recognit..

[4]  Hui Wang,et al.  An active contour model and its algorithms with local and global Gaussian distribution fitting energies , 2014, Inf. Sci..

[5]  Ling Zhang,et al.  A novel active contour model for image segmentation using local and global region-based information , 2017, Machine Vision and Applications.

[6]  Qiang Chen,et al.  Active contours driven by local likelihood image fitting energy for image segmentation , 2015, Inf. Sci..

[7]  V. Rajinikanth,et al.  Entropy based segmentation of tumor from brain MR images - a study with teaching learning based optimization , 2017, Pattern Recognit. Lett..

[8]  Siqi Chen,et al.  Level set segmentation with both shape and intensity priors , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[9]  Stefano Messelodi,et al.  A New Region-based Active Contour Model for Object Segmentation , 2015, Journal of Mathematical Imaging and Vision.

[10]  Chunming Li,et al.  A Level Set Method for Image Segmentation in the Presence of Intensity Inhomogeneities With Application to MRI , 2011, IEEE Transactions on Image Processing.

[11]  Daniel Cremers,et al.  Kernel Density Estimation and Intrinsic Alignment for Shape Priors in Level Set Segmentation , 2006, International Journal of Computer Vision.

[12]  Lei Zhang,et al.  Active contours with selective local or global segmentation: A new formulation and level set method , 2010, Image Vis. Comput..

[13]  Yufei Chen,et al.  Region scalable active contour model with global constraint , 2017, Knowl. Based Syst..

[14]  Yiquan Wu,et al.  Active contours driven by median global image fitting energy for SAR river image segmentation , 2017, Digit. Signal Process..

[15]  Chunming Li,et al.  Computerized Medical Imaging and Graphics Active Contours Driven by Local and Global Intensity Fitting Energy with Application to Brain Mr Image Segmentation , 2022 .

[16]  Chunming Li,et al.  Distance Regularized Level Set Evolution and Its Application to Image Segmentation , 2010, IEEE Transactions on Image Processing.

[17]  Ying Li,et al.  A novel active contour model for image segmentation using distance regularization term , 2013, Comput. Math. Appl..

[18]  Linfang Xiao,et al.  Active contours driven by region-scalable fitting and optimized Laplacian of Gaussian energy for image segmentation , 2017, Signal Process..

[19]  Marcin Ciecholewski,et al.  An edge-based active contour model using an inflation/deflation force with a damping coefficient , 2016, Expert Syst. Appl..

[20]  Xin Yang,et al.  Active contour model driven by local histogram fitting energy , 2013, Pattern Recognit. Lett..

[21]  Qiang Zheng,et al.  Active contour model driven by linear speed function for local segmentation with robust initialization and applications in MR brain images , 2014, Signal Process..

[22]  Ke Chen,et al.  Active contours textural and inhomogeneous object extraction , 2016, Pattern Recognit..

[23]  Zemin Ren,et al.  Adaptive active contour model driven by fractional order fitting energy , 2015, Signal Process..

[24]  Michael Unser,et al.  Snakes with tangent-based control and energies for bioimage analysis , 2014, 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI).

[25]  D. Selvathi,et al.  Phase based distance regularized level set for the segmentation of ultrasound kidney images , 2017, Pattern Recognit. Lett..

[26]  Ke Chen,et al.  Automated Vessel Segmentation Using Infinite Perimeter Active Contour Model with Hybrid Region Information with Application to Retinal Images , 2015, IEEE Transactions on Medical Imaging.

[27]  Qiang Chen,et al.  Robust noise region-based active contour model via local similarity factor for image segmentation , 2017, Pattern Recognit..

[28]  Shigang Liu,et al.  A local region-based Chan-Vese model for image segmentation , 2012, Pattern Recognit..

[29]  Fabrice Heitz,et al.  Multi-Reference Shape Priors for Active Contours , 2008, International Journal of Computer Vision.

[30]  Maoguo Gong,et al.  Novel fuzzy active contour model with kernel metric for image segmentation , 2015, Appl. Soft Comput..

[31]  Annupan Rodtook,et al.  Automatic initialization of active contours and level set method in ultrasound images of breast abnormalities , 2018, Pattern Recognit..

[32]  Yong Gan,et al.  An active contour model based on fused texture features for image segmentation , 2015, Neurocomputing.

[33]  Weifeng Li,et al.  Active contours driven by local and global probability distributions , 2013, J. Vis. Commun. Image Represent..

[34]  Sajid Hussain,et al.  Active contours for image segmentation using complex domain-based approach , 2016, IET Image Process..

[35]  Emmanuel Viennet,et al.  Efficient segmentation with the convex local-global fuzzy Gaussian distribution active contour for medical applications , 2015, Annals of Mathematics and Artificial Intelligence.

[36]  Chang-Tsun Li,et al.  Weighted Level Set Evolution Based on Local Edge Features for Medical Image Segmentation , 2017, IEEE Transactions on Image Processing.

[37]  Ertunc Erdil,et al.  Nonparametric Joint Shape and Feature Priors for Image Segmentation , 2017, IEEE Transactions on Image Processing.

[38]  Müjdat Çetin,et al.  Combining learning-based intensity distributions with nonparametric shape priors for image segmentation , 2014, Signal Image Video Process..

[39]  Yihong Gong,et al.  Active contour model based on local and global intensity information for medical image segmentation , 2016, Neurocomputing.

[40]  Ashish Ghosh,et al.  Image co-segmentation using dual active contours , 2018, Appl. Soft Comput..