Improving Medical Image Segmentation Techniques using Multiphase Level Set Approach Via Bias Correction
Pradeep Kumar1, Rajat Chaudhary2, Ambika Aggarwal3, Prem Singh4, Ravi Tomar5
1Pradeep Kumar, Information Security and Management, Uttarakhand Technical University/ Dehradun Institute of Technology/Dehradun, India.
2Rajat Chaudhary, Information Security and Management, Uttarakhand Technical University/ Dehradun Institute of Technology/Dehradun, India.
3Ambika Aggarwal, Information Security and Management, Uttarakhand Technical University/ Dehradun Institute of Technology / Dehradun, India.
4Mr. Prem Singh, Information Security and Management, Uttarakhand Technical University/Dehradun Institute of Technology / Dehradun, India.
5Mr. Ravi Tomar, Computer Science and Engineering, Uttarakhand Technical University/ Dehradun Institute of Technology/Dehradun, India.
Manuscript received on May 17, 2012. | Revised Manuscript received on June 22, 2012. | Manuscript published on June 30, 2012. | PP: 285-289 | Volume-1 Issue-5, June 2012. | Retrieval Number: E0512061512/2012©BEIESP
Open Access | Ethics and Policies | Cite
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In this paper, we present a new variational formulation for geometric active contours that forces the level set function to be close to a signed distance function, and therefore completely eliminates the need of the costly re-initialization procedure. Our variational formulation consists of an internal energy term that penalizes the deviation of the levelset function from a signed distance function, and an external energy term that drives the motion of the zero level set toward the desired image features, such as object boundaries. The resulting evolution of the level set function is the gradient flow that minimizes the overall energy functional.
Keywords: Image segmentation, level set formulation, Gradient, bias field and MRI