scholarly journals Adaptive Segmentation of Remote Sensing Images Based on Global Spatial Information

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2385 ◽  
Author(s):  
Muqing Li ◽  
Luping Xu ◽  
Shan Gao ◽  
Na Xu ◽  
Bo Yan
Keyword(s):  
Image Segmentation ◽  
Euclidean Distance ◽  
Clustering Algorithm ◽  
Spatial Information ◽  
Distance Measure ◽  
Target Function ◽  
Descent Method ◽  
Gradient Descent Method ◽  
Clustering Center ◽  
Membership Information

The problem of image segmentation can be reduced to the clustering of pixels in the intensity space. The traditional fuzzy c-means algorithm only uses pixel membership information and does not make full use of spatial information around the pixel, so it is not ideal for noise reduction. Therefore, this paper proposes a clustering algorithm based on spatial information to improve the anti-noise and accuracy of image segmentation. Firstly, the image is roughly clustered using the improved Lévy grey wolf optimization algorithm (LGWO) to obtain the initial clustering center. Secondly, the neighborhood and non-neighborhood information around the pixel is added into the target function as spatial information, the weight between the pixel information and non-neighborhood spatial information is adjusted by information entropy, and the traditional Euclidean distance is replaced by the improved distance measure. Finally, the objective function is optimized by the gradient descent method to segment the image correctly.

scholarly journals MRI Image Segmentation Using Conditional Spatial FCM Based on Kernel-Induced Distance Measure

2018 ◽  
Vol 8 (3) ◽  
pp. 2985-2990 ◽  
Author(s):  
B. Gharnali ◽  
S. Alipour
Keyword(s):  
Image Segmentation ◽  
Membership Function ◽  
Clustering Algorithm ◽  
Spatial Information ◽  
Distance Measure ◽  
Auxiliary Variable ◽  
Medical Image Segmentation ◽  
Intensity Inhomogeneity ◽  
Fuzzy Membership Function ◽  
Fuzzy C Means

Fuzzy C-means (FCM) clustering is the widest spread clustering approach for medical image segmentation because of its robust characteristics for data classification. But, it does not fully utilize the spatial information and is therefore very sensitive to noise and intensity inhomogeneity in magnetic resonance imaging (MRI). In this paper, we propose a conditional spatial kernel fuzzy C-means (CSKFCM) clustering algorithm to overcome the mentioned problem. The approach consists of two successive stages. First stage is achieved through the incorporation of local spatial interaction among adjacent pixels in the fuzzy membership function imposed by an auxiliary variable associated with each pixel. The variable describes the involvement level of each pixel for construction of membership functions and different clusters. Then, we adapted a kernel-induced distance to replace the original Euclidean distance in the FCM, which is shown to be more robust than FCM. The problem of sensitivity to noise and intensity inhomogeneity in MRI data is effectively reduced by incorporating a kernel-induced distance metric and local spatial information into a weighted membership function. The experimental results show that the proposed algorithm has advantages in accuracy and robustness against noise in comparison with the FCM, SFCM and CSFCM methods on MRI brain images.

Spatial Information Based on the Multi-Objective Programming of Fuzzy Kernel Clustering Image Segmentation Algorithm

2013 ◽  
Vol 791-793 ◽  
pp. 1337-1340
Author(s):  
Xue Zhang Zhao ◽  
Ming Qi ◽  
Yong Yi Feng
Keyword(s):  
Image Segmentation ◽  
Clustering Algorithm ◽  
Spatial Information ◽  
Segmentation Algorithm ◽  
Multi Objective ◽  
Multi Objective Programming ◽  
Kernel Clustering ◽  
Noise Image ◽  
The Relationship ◽  
Image Segmentation Algorithm

Fuzzy kernel clustering algorithm is a combination of unsupervised clustering and fuzzy set of the concept of image segmentation techniques, But the algorithm is sensitive to initial value, to a large extent dependent on the initial clustering center of choice, and easy to converge to local minimum values, when used in image segmentation, membership of the calculation only consider the current pixel values in the image, and did not consider the relationship between neighborhood pixels, and so on segmentation contains noise image is not ideal. This paper puts forward an improved fuzzy kernel clustering image segmentation algorithm, the multi-objective problem, change the single objective problem to increase the secondary goals concerning membership functions, Then add the constraint information space; Finally, using spatial neighborhood pixels corrected membership degree of the current pixel. The experimental results show that the algorithm effectively avoids the algorithm converges to local extremism and the stagnation of the iterative process will appear problem, significantly lower iterative times, and has good robustness and adaptability.

scholarly journals Robust Deep Kernel-Based Fuzzy Clustering With Spatial Information for Image Segmentation

2021 ◽  
Author(s):  
Lujia Lei ◽  
Chengmao Wu ◽  
Xiaoping Tian
Keyword(s):  
Image Segmentation ◽  
Kernel Function ◽  
Loss Function ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Spatial Information ◽  
Clustering Algorithms ◽  
Noise Robustness ◽  
Neighborhood Information ◽  
Classical Image

Abstract Clustering algorithms with deep neural network have attracted wide attention of scholars. A deep fuzzy K-means clustering algorithm model with adaptive loss function and entropy regularization (DFKM) is proposed by combining automatic encoder and clustering algorithm. Although it introduces adaptive loss function and entropy regularization to improve the robustness of the model, its segmentation effect is not ideal for high noise; At the same time, its model does not use a convolutional auto-encoder, which is not suitable for high-dimensional images.Therefore, on the basis of DFKM, this paper focus on image segmentation, combine neighborhood median and mean information of current pixel, introduce neighborhood information of membership degree, and extend Euclidean distance to kernel space by using kernel function, propose a dual-neighborhood information constrained deep fuzzy clustering based on kernel function (KDFKMS). A large number of experimental results show that compared with DFKM and classical image segmentation algorithms, this algorithm has stronger anti-noise robustness.

An Improved C-Means Clustering for Image Segmentation

2014 ◽  
Vol 981 ◽  
pp. 344-347
Author(s):  
Tai Fa Zhang
Keyword(s):  
Image Segmentation ◽  
Clustering Algorithm ◽  
Research Area ◽  
Image Process ◽  
Main Research ◽  
Clustering Center

Image segmentation is an important part of the image process, and it is also the current hot and focus in image research. How to achieve better segmentation results are dominating targets of researchers. Currently, image segmentation based on clustering is the main research area. Firstly, this paper introduces the traditional C-means clustering algorithm and its characteristic has been analyzed. Then, the initial clustering center and the number are selected using the histogram. Finally, the image is converted from the RGB space to Lab space for clustering, and it has improved the accuracy and efficiency of image segmentation.

scholarly journals Spatial information of fuzzy clustering based mean best artificial bee colony algorithm for phantom brain image segmentation

2021 ◽  
Vol 11 (5) ◽  
pp. 4050
Author(s):  
Waleed Alomoush ◽  
Ayat Alrosan ◽  
Ammar Almomani ◽  
Khalid Alissa ◽  
Osama A. Khashan ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Fuzzy Clustering ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Clustering Algorithm ◽  
Spatial Information ◽  
Medical Image Segmentation ◽  
Local Optimum ◽  
Brain Image ◽  
Bee Colony

Fuzzy c-means algorithm (FCM) is among the most commonly used in the medical image segmentation process. Nevertheless, the traditional FCM clustering approach has been several weaknesses such as noise sensitivity and stuck in local optimum, due to FCM hasn’t able to consider the information of contextual. To solve FCM problems, this paper presented spatial information of fuzzy clustering-based mean best artificial bee colony algorithm, which is called SFCM-MeanABC. This proposed approach is used contextual information in the spatial fuzzy clustering algorithm to reduce sensitivity to noise and its used MeanABC capability of balancing between exploration and exploitation that is explore the positive and negative directions in search space to find the best solutions, which leads to avoiding stuck in a local optimum. The experiments are carried out on two kinds of brain images the Phantom MRI brain image with a different level of noise and simulated image. The performance of the SFCM-MeanABC approach shows promising results compared with SFCM-ABC and other stats of the arts.

Brain Magnetic Resonance Imaging Segmentation Using Possibilistic Clustering Algorithm Combined with Context Constraints

2020 ◽  
Vol 10 (7) ◽  
pp. 1669-1674
Author(s):  
Zixuan Cheng ◽  
Li Liu
Keyword(s):  
Image Segmentation ◽  
Iterative Process ◽  
Clustering Algorithm ◽  
Spatial Information ◽  
Brain Magnetic Resonance Imaging ◽  
Medical Image Segmentation ◽  
Clustering Methods ◽  
Possibilistic Clustering ◽  
Image Pixels ◽  
Context Constraints

Because the FCM method is simple and effective, a series of research results based on this method are widely used in medical image segmentation. Compared with the traditional FCM, the probability clustering (PCM) algorithm cancels the constraint on the normalization of each sample membership degree in the iterative process, and the clustering effect of the method is improved within a certain range. However, the above two methods only use the gray value of the image pixels in the iterative process, ignoring the context constraint relationship between the high-dimensional image pixels. The two are easily affected by image noise during the segmentation process, resulting in poor robustness, which will affect the segmentation accuracy in practical applications. In order to alleviate this problem, this paper introduces the context constraint information of image based on PCM, and proposes a PCM algorithm that combines context constraints (CCPCM) and successfully applies it to human brain MR image segmentation to further improve the noise immunity of the new algorithm. Expand the applicability of new algorithms in the medical field. Through simulation results on medical images, it is found that compared with the previous classical clustering methods, such as FCM, PCM, etc., the CCPCM has better anti-interference to different noises, and the segmentation boundary is clearer. At the same time, CCPCM algorithm introduces the spatial neighbor information adaptive weighting mechanism in the clustering process, which can adaptively adjust the constraint weight of spatial information and optimize the clustering process, thus improving the segmentation efficiency.

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