A Normalized Local Binary Fitting Model for Image Segmentation

2012 ◽  
Author(s):  
Yali Peng ◽  
Fang Liu ◽  
Shigang Liu
Keyword(s):  
Image Segmentation ◽  
Local Binary Fitting Model ◽  
Fitting Model

CS-LBF Improved Model for 3D Medical Image Segmentation

2013 ◽  
Vol 303-306 ◽  
pp. 2272-2279 ◽  
Author(s):  
Wen Cang Zhao ◽  
Jun Bo Zhang
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Contrast Ratio ◽  
Three Dimensional ◽  
Medical Image Segmentation ◽  
Low Contrast ◽  
Shape Constraint ◽  
Local Binary Fitting Model ◽  
Improved Model ◽  
Fitting Model

This paper presents an algorithm for three-dimensional medical image segmentation based on the Contrast and Shape Constrained Local Binary Fitting improved model. Due to Local Binary Fitting model is sensitive to initialization and easy to fall into local extreme value, the new algorithm adds contrast constraint term to the Local Binary Fitting model, aiming at solving the common existed problem of inconsistent brightness and low contrast ratio. Adding shape constraint term can improve the original Local Binary Fitting model by constructing shape constraint energy field around the average shape by the level set method to deal with the leak of deformation curve. In order to promote the speed of model evolution, the kernel function is simplified. Two-dimensional Contrast and Shape Constrained Local Binary Fitting model is then extended to three-dimensional and a three-dimensional dental pulp image is segmented. Experimental results show that the segmentation accuracy, the connection degree and the efficiency of the new method are greatly improved compared to original LBF model.

scholarly journals A Novel Adaptive Level Set Segmentation Method

2014 ◽  
Vol 2014 ◽  
pp. 1-8
Author(s):  
Yazhong Lin ◽  
Qian Zheng ◽  
Jiaqiang Chen ◽  
Qian Cai ◽  
Qianjin Feng
Keyword(s):  
Level Set ◽  
Performance Indicators ◽  
Spatial Information ◽  
Initial Contour ◽  
Segmentation Method ◽  
Improved Method ◽  
Local Binary Fitting Model ◽  
Segmentation Of Images ◽  
Accuracy Speed ◽  
Fitting Model

The adaptive distance preserving level set (ADPLS) method is fast and not dependent on the initial contour for the segmentation of images with intensity inhomogeneity, but it often leads to segmentation with compromised accuracy. And the local binary fitting model (LBF) method can achieve segmentation with higher accuracy but with low speed and sensitivity to initial contour placements. In this paper, a novel and adaptive fusing level set method has been presented to combine the desirable properties of these two methods, respectively. In the proposed method, the weights of the ADPLS and LBF are automatically adjusted according to the spatial information of the image. Experimental results show that the comprehensive performance indicators, such as accuracy, speed, and stability, can be significantly improved by using this improved method.

scholarly journals Incorporating a Local Binary Fitting Model into a Maximum Regional Difference Model for Extracting Microscopic Information under Complex Conditions

2012 ◽  
Vol 2012 ◽  
pp. 1-10
Author(s):  
Chunyan Yao ◽  
Jianwei Zhang ◽  
Min Chen ◽  
Qiu Guan ◽  
Massimo Scalia
Keyword(s):  
Regional Difference ◽  
Driving Forces ◽  
Cell Segmentation ◽  
The Novel ◽  
Difference Model ◽  
Intensity Information ◽  
Local Binary Fitting Model ◽  
Novel Model ◽  
Fitting Model ◽  
Global Fitting

This paper presents a novel region-based method for extracting useful information from microscopic images under complex conditions. It is especially used for blood cell segmentation and statistical analysis. The active model detects several inner and outer contours of an object from its background. The method incorporates a local binary fitting model into a maximum regional difference model. It utilizes both local and global intensity information as the driving forces of the contour model on the principle of the largest regional difference. The local and global fitting forces ensure that local dissimilarities can be captured and globally different areas can be segmented, respectively. By combining the advantages of local and global information, the motion of the contour is driven by the mixed fitting force, which is composed of the local and global fitting term in the energy function. Experiments are carried out in the laboratory, and results show that the novel model can yield good performances for microscopic image analysis.

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