[1]陆海青,葛洪伟.自适应灰度加权的鲁棒模糊C均值图像分割[J].智能系统学报,2018,13(04):584-593.[doi:10.11992/tis.201701008]
 LU Haiqing,GE Hongwei.Adaptive gray-weighted robust fuzzy C-means algorithm for image segmentation[J].CAAI Transactions on Intelligent Systems,2018,13(04):584-593.[doi:10.11992/tis.201701008]
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自适应灰度加权的鲁棒模糊C均值图像分割(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第13卷
期数:
2018年04期
页码:
584-593
栏目:
出版日期:
2018-07-05

文章信息/Info

Title:
Adaptive gray-weighted robust fuzzy C-means algorithm for image segmentation
作者:
陆海青1 葛洪伟12
1. 江南大学 物联网工程学院, 江苏 无锡 214122;
2. 江南大学 轻工过程先进控制教育部重点实验室, 江苏 无锡 214122
Author(s):
LU Haiqing1 GE Hongwei12
1. School of Internet of Things, Jiangnan University, Wuxi 214122, China;
2. Ministry of Education Key Laboratory of Advanced Process Control for Light Industry, Jiangnan University, Wuxi 214122, China
关键词:
模糊C均值图像分割自适应灰度加权空间信息相似距离抗噪性
Keywords:
fuzzy C-meansimage segmentationadaptive gray weightspatial informationsimilarity distancenoise resistance
分类号:
TP391.4
DOI:
10.11992/tis.201701008
摘要:
针对传统模糊C均值(fuzzy C-means,FCM)算法以及结合空间信息的相关改进算法分割精度较低、对噪声敏感的问题,提出一种自适应灰度加权的鲁棒模糊C均值图像分割算法。首先,通过定义像素间的局部灰度相似性测度来反映各像素对局部邻域的影响程度,并根据邻域窗口中各像素的灰度差异,利用指数函数进一步控制邻域像素的影响权重,实现像素灰度的自适应加权,从而提高像素灰度计算的准确性。其次,构造出一种改进的距离测度代替传统的欧氏距离,用于计算各像素与聚类中心之间的相似距离,增强算法对噪声和异常值的鲁棒性。最后,将提出的自适应灰度加权方法与改进的距离测度应用到FCM算法中,实现图像分割。实验结果表明,该算法需根据图像噪声的强度适当地选取邻域窗口大小,在此条件下算法能够取得较优的分割效果和运行效率,且对噪声具有较强的鲁棒性。
Abstract:
The traditional fuzzy C-means (FCM) algorithm and its corresponding improved algorithm that is combined with spatial information have low segmentation accuracy and poor robustness to noise. To address these defects, we propose a robust FCM image segmentation algorithm based on adaptive gray-weighting. First, we define a local grayscale similarity measure for pixels to reflect the influence of all pixels on the local neighborhood. Regarding the grayscale difference between pixels in a neighborhood window, we utilize an exponential function to further control the influence weight of a neighborhood pixel and realize adaptive weighting of the pixel grayscale to improve its calculation accuracy Next, to strengthen the robustness of the algorithm to noise and outliers, we use an improved distance measure to replace the traditional Euclidean distance and use it to calculate the similarity distance between the pixels and the clustering center. Finally, we apply this new method based on adaptive gray weight and enhanced distance measurement to an FCM algorithm for image segmentation. Our experimental results show that, for the algorithm, the size of the neighborhood window must be properly selected on basis of the noise intensity of an image. Under this condition, an excellent segmentation effect and operational efficiency can be achieved, in addition to excellent robustness to noise.

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备注/Memo

备注/Memo:
收稿日期:2017-01-11。
基金项目:江苏省普通高校研究生科研创新计划项目(KYLX16_0781,KYLX16_0782);江苏高校优势学科建设工程资助项目(PAPD).
作者简介:陆海青,男,1992年生,硕士研究生,主要研究方向为图像处理和模式识别;葛洪伟,男,1967年生,教授,博士生导师,博士,主要研究方向为人工智能与模式识别、机器学习、图像处理与分析。主持和承担国家自然科学基金等国家级项目和省部级项目近20项,获省部级科技进步奖多项。发表学术论文百余篇。
通讯作者:葛洪伟.E-mail:ghw8601@163.com.
更新日期/Last Update: 2018-08-25