基于信息熵的自适应尺度活动轮廓图像分割模型 -- 西北工业大学学报,2017,35(2):286-291
论文:2017,Vol:35,Issue(2):286-291
引用本文:
蔡青, 刘慧英, 孙景峰, 李靖, 周三平. 基于信息熵的自适应尺度活动轮廓图像分割模型[J]. 西北工业大学学报
Cai Qing, Liu Huiying, Sun Jingfeng, Li Jing, Zhou Sanping. An Adaptive Scale Active Contour Model Based on Information Entropy for Image Segmentation[J]. Northwestern polytechnical university

基于信息熵的自适应尺度活动轮廓图像分割模型
蔡青1, 刘慧英1, 孙景峰1, 李靖2, 周三平3
1. 西北工业大学 自动化学院, 陕西 西安 710072;
2. 西北工业大学 机电学院, 陕西 西安 710072;
3. 西安交通大学 人工智能与机器人研究所, 陕西 西安 710049
摘要:
针对固定尺度活动轮廓模型无法快速准确分割灰度不均匀图像的问题,提出了一种基于信息熵的自适应尺度活动轮廓图像分割模型。首先,利用最大后验概率(MAP)以及贝叶斯分类准则,提出了一种新型能量泛函,提高了模型对灰度信息的提取能力,进而极大提高了模型对灰度不均匀图像的分割准确度。其次,利用图像信息熵构造了自适应尺度算子,使模型能根据图像灰度不均程度自动调整尺度,提高了模型对灰度不均匀图像的分割速度。最后,为验证文中模型的优越性,将该模型与LGDF模型进行了对比,并通过分割时间、迭代次数以及相似度等指标,对分割结果进行了客观、定量分析。最终结果表明,该模型不但对初始轮廓具有较高鲁棒性,而且对灰度不均匀图像具有较高的分割准确性与分割效率。
关键词:    图像分割    活动轮廓模型    灰度不均匀图像    信息熵    自适应尺度算子   
An Adaptive Scale Active Contour Model Based on Information Entropy for Image Segmentation
Cai Qing1, Liu Huiying1, Sun Jingfeng1, Li Jing2, Zhou Sanping3
1. School of Automation, Northwestern Polytechnical University, Xi'an 710072, China;
2. School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, China;
3. Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an 710049, China
Abstract:
In view of the problem that the fixed scale active contour model cannot quickly and accurately segment images with intensity inhomogeneity, an adaptive scale active contour model based on the information entropy is proposed for image segmentation. Firstly, we put forward a novel energy function by using Maximum Posterior Probability (MAP) and Bayes classification criterion, which greatly improve the ability to extract the image intensity information and the segmentation accuracy for inhomogeneous images. Secondly, we construct an adaptive scale operator by using the image information entropy to let the model can automatically adjust the scale according to the intensity inhomogeneity degree of the image, which improves the segmentation speed of the model. Finally, in order to verify the superiority of our model, we make a comparison between our model and LGDF model, and also make an objective and quantitative analysis of the segmentation results by using the segmentation time, the number of iterations and the similarity index. The final results show that the proposed model not only has high robustness to the initial contour, but also has high accuracy and efficiency in segmenting images with intensity inhomogeneity.
Key words:    image segmentation    active contour model    images with intensity inhomogeneity    information entropy    adaptive scale operator   
收稿日期: 2016-10-13     修回日期:
DOI:
基金项目: 中央高校基本科研业务费专项资金(3102016ZY013)资助
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作者简介: 蔡青(1988-),西北工业大学博士研究生,主要从事图像处理及模式识别的研究。
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