Prediction of LSE via Reaction Dispersion
D. Devi Sirisha1, Satya Naresh2
1D. Devi Sirisha, M. Tech Student. DOD Trimurthulu, Near KIMS Hospital,  Amalapuram, (Andhra Pradesh), India.
2CH. Satya Naresh, Assistant Professor, DOD Trimurthulu, Near KIMS Hospital,  Amalapuram, (Andhra Pradesh), India.
Manuscript received on September 26, 2014. | Revised Manuscript received on October 08, 2014. | Manuscript published on October 30, 2014. | PP: 155-160  | Volume-4 Issue-1, October 2014. | Retrieval Number:  A3474104114/2013©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 (

Abstract: A dispersion term is hosted into LSE, resulting in a RDLSE equation, to which a piecewise constant solution can be derived. This project presents an innovative reaction-dispersion (RD) method for implicit active outlines, which is completely free of the costly re-initialization procedure in level set evolution (LSE). In order to have a balanced statistical result of the RD based LSE, we recommend a two-step splitting method (TSSM) to iteratively crack the RD-LSE equation: first iterating the LSE equation, and then solving the dispersion equation. The second step regularizes the level set function obtained in the first step to ensure stability, and thus the complex and costly re-initialization procedure is completely eliminated from LSE. By successfully applying dispersion to LSE, the RD-LSE model is stable by means of the simple finite difference method, which is very easy to implement. The proposed RD method can be generalized to solve the LSE for both variation level set method and PDE-based level set method. The RD-LSE method shows appropriate noble concert on boundary anti-leakage, and it can be voluntarily prolonged to high dimensional level set method. The extensive and promising experimental results on synthetic and real images validate the effectiveness of the proposed RD-LSE approach.
Keywords:  RD-LSE, PDE, TSSM.