[1]LU Haiqing,GE Hongwei.Adaptive gray-weighted robust fuzzy C-means algorithm for image segmentation[J].CAAI Transactions on Intelligent Systems,2018,13(4):584-593.[doi:10.11992/tis.201701008]
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Adaptive gray-weighted robust fuzzy C-means algorithm for image segmentation

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 13 Number of periods: 2018 4 Page number: 584-593 Column: 学术论文—机器感知与模式识别 Public date: 2018-07-05
Title:
Adaptive gray-weighted robust fuzzy C-means algorithm for image segmentation
Author(s):
LU Haiqing1;  GE Hongwei1; 2
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
Keywords:
fuzzy C-means; image segmentation; adaptive gray weight; spatial information; similarity distance; noise resistance
CLC:
TP391.4
DOI:
10.11992/tis.201701008
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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