A novel self-learning weighted fuzzy local information clustering algorithm integrating local and non-local spatial information for noise image segmentation

Song, Qiuyu; Wu, Chengmao; Tian, Xiaoping; Song, Yue; Guo, Xiaokang

doi:10.1007/s10489-021-02722-7

Cited by 13 publications

(6 citation statements)

References 48 publications

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“…The two clustering algorithms based on evidence theory are ECM [19] and DEC [23]. The seven clustering algorithms based on fuzzy theory are FCM S1 [12], FCM S2 [12], FLICM [14], FCM NLS [13], FRFCM [15], FCM SICM [16], and IFLICMLNLI [18]. In ECM, set penalization exponent α = 2 according to [19].…”

Section: Comparison Algorithms and Parameters Settingmentioning

confidence: 99%

“…However, assigning the same weight to these two different distances may incorrectly magnify the importance of the neighborhood information. Therefore, Song et al [18] proposed a self-learning weighted fuzzy local information clustering integrating local and nonlocal information. This algorithm calculates distance weights by self-learning and adaptively balances the anti-noise ability and detail retention ability.…”

Section: Introductionmentioning

confidence: 99%

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Robust Evidence C-Means Clustering Combining Spatial Information for Image Segmentation

Lan

et al. 2023

Preprint

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Although evidence c-means clustering (ECM) based on evidence theory overcomes the limitations of fuzzy theory to some extent and improves the capability of fuzzy c-means clustering (FCM) to express and process the uncertainty of information, the ECM does not consider the spatial information of pixels, which makes it to be unable to effectively deal with noise pixels. Applying ECM directly to image segmentation cannot obtain satisfactory results. This paper proposes a robust evidence c-means clustering combining spatial information for image segmentation algorithm. Firstly, an adaptive noise distance is constructed by using the local information of pixels to improve the ability to detect noise points. Secondly, the pixel’s original, local and non-local information are introduced into the objective function through adaptive weights to enhance the robustness to noise. Then, the entropy of pixel membership degree is used to design an adaptive parameter to solve the problem of distance parameter selection in credal c-means clustering (CCM). Finally, the Dempster’s rule of combination was improved by introducing spatial neighborhood information, which is used to assign the pixels belonging to the meta-cluster and the noise cluster into the singleton cluster. Experiments on synthetic images, real images and remote sensing SAR images demonstrate that the proposed algorithm not only suppress noise effectively, but also retain the details of the image. Both the segmentation visual effect and evaluation indexes indicate its effectiveness in image segmentation.

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Section: Comparison Algorithms and Parameters Settingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust Evidence C-Means Clustering Combining Spatial Information for Image Segmentation

Lan

et al. 2023

Preprint

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show abstract

“…In Equation (25), A and B are the coordinates of two points in space, and c is the cth point in space, | A c − B c | is the distance between any two points in the space, and z is the zth times. The meaning of the remaining letters is the same as the above.…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

“…(4) Chebyshev distance Chebyshev distance is a measure in vector space, which was used in chess before [25,26]. It is the maximum distance between two points, and it is calculated by…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

Intelligent Online Monitoring and Remote Verification of Gateway Meters under the Embedded Sensor and Clustering Algorithm

Wang

et al. 2022

Wireless Communications and Mobile Computing

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Since gateway meters cannot simultaneously realize online monitoring and remote verification, the study is aimed at exploring the intelligent online monitoring and remote verification of the gateway meter. First, the similarity and related evaluation indexes of embedded sensors are analyzed based on the relevant theories, like the theories of the embedded sensor and clustering algorithm. Second, the gateway meter is tested on the standard vibration test bed, and the accuracy, timeliness, and environmental adaptability of its intelligent online system are tested. Finally, the remote verification of the gateway meter is carried out from three aspects: the error/load value, secondary voltage drop, and the admittance test. The results show that when the intelligent online monitoring ability of the gateway meter is tested on the standard vibration test bed, the error of the same pilot is controlled between 0.1 mm after multiple peak tests, and the error is within the allowable range. In the intelligent online monitoring system based on the embedded sensor and clustering algorithm, the vibration acceleration is -0.6 cm/s~0.3 cm/s, the speed is -1 cm/s~1 cm/s, and the displacement fluctuates between -0.8 cm/s~0.8 cm/s. This shows that the intelligent online monitoring system can meet the performance requirements of online monitoring. In the process of remote verification of gateway meters, the active error and reactive error are 0.2% mm. The results of the secondary voltage drop and the admittance test show that the relevant technical indexes of the system meet the expected requirements. Therefore, the intelligent online monitoring and remote verification of gateway meters are discussed based on the embedded sensor and clustering algorithm, which provides a reference for the rapid development of gateway meters.

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“…To segment medical images used, the fuzzy C-means method, a new FCM method that heavily relies on the neighbourhood means to determine the goal function. The enhanced FCM algorithm revealed non-local information in the neighbour function (Song et al, 2022). The modified genetic algorithm crossover and mutation process are used to process the input picture by the algorithm, which is used to make the final segmentation result.…”

Section: Introductionmentioning

confidence: 99%

Brain Tumour Region Extraction Using Novel Self-Organising Map-Based KFCM Algorithm

Reddy¹,

Ramashri²,

Krishna³

2022

JST

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Medical professionals need help finding tumours in the ground truth image of the brain because the tumours’ location, contrast, intensity, size, and shape vary between images because of different acquisition methods, modalities, and the patient’s age. The medical examiner has difficulty manually separating a tumour from other parts of a Magnetic Resonance Imaging (MRI) image. Many semi- and fully automated brain tumour detection systems have been written about in the literature, and they keep improving. The segmentation literature has seen several transformations throughout the years. An in-depth examination of these methods will be the focus of this investigation. We look at the most recent soft computing technologies used in MRI brain analysis through several review papers. This study looks at Self-Organising maps (SOM) with K-means and the kernel Fuzzy c-means (KFCM) method for segmenting them. The suggested SOM networks were first compared to K-means analysis in an experiment based on datasets with well-known cluster solutions. Later, the SOM is combined with KFCM, reducing time complexity and producing more accurate results than other methods. Experiments show that skewed data improves networks’ performance with more SOMs. Finally, performance measures in real-time datasets are analysed using machine learning approaches. The results show that the proposed algorithm has good sensitivity and better accuracy than k-means and other state-of-art methods.

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A novel self-learning weighted fuzzy local information clustering algorithm integrating local and non-local spatial information for noise image segmentation

Cited by 13 publications

References 48 publications

Robust Evidence C-Means Clustering Combining Spatial Information for Image Segmentation

Robust Evidence C-Means Clustering Combining Spatial Information for Image Segmentation

Intelligent Online Monitoring and Remote Verification of Gateway Meters under the Embedded Sensor and Clustering Algorithm

Brain Tumour Region Extraction Using Novel Self-Organising Map-Based KFCM Algorithm

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