Nonlinear medical image restoration method based on canonical variation model(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2017年第11期
- Page:
- 1117-1123
- Research Field:
- 医学影像物理
- Publishing date:
Info
- Title:
- Nonlinear medical image restoration method based on canonical variation model
- Author(s):
- WANG Jing; HAN Xue; LIU Hongmin; WANG Zhiheng
- School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo 454003, China
- Keywords:
- ; Keywords: medical image restoration; degeneration; total variation; alternating split Bregman; threshold operator
- PACS:
- R312；TP391
- DOI:
- DOI:10.3969/j.issn.1005-202X.2017.11.008
- Abstract:
- Abstract: The existing medical image restoration methods have some problems such as loss of detail, blurred boundary and algorithm computational complexity. Herein, we adopt an efficient total variation regularized iterative method to deal with blurred and noisy degraded medical images. This proposed method combines the characteristics of ROF model to preserve edges and structures, makes full use of the gradient relationship between the image energy, and adds fuzzy kernel operators to the classical variational denoising model. For the convex functional model, the idea of variable separation is adopted, and the two penalty item and relaxation variables are introduced to decompose the unconstrained optimization problem into a series of subproblems, and combined with the alternating split Bregman technique and frame control of the reconciliation, the function is iterated directly. Meanwhile, the threshold operator and contraction technique are introduced to optimize the subproblems for maintaining important edges and details of medical images and overcoming the computational complexity of traditional methods. The simulation results show that compared with the traditional restoration methods, this proposed method improves the signal-to-noise ratio of the image, significantly reduces mean square error, overcomes the ringing effect, and improves the image visual effect, which proves the validity of the method. The method can be more effective applied to clinical diagnosis and subsequent segmentation for it has a good stability and a fast convergence.
Last Update: 2017-11-23