Robust fuzzy c-means clustering algorithm with adaptive spatial &amp; intensity constraint and membership linking for noise image segmentation

Wang, Qingsheng; Wang, Xiaopeng; Chao, Fang Lin; Yang, Wenting

doi:10.1016/j.asoc.2020.106318

Cited by 74 publications

(22 citation statements)

References 35 publications

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“…Our work fucuses on segment a picture which contains a lot of noises and need to be segmented into several parts. Previous studies have paid a lot of attention on the robustness of FCM when using on image segmentation [13], [14], [16], [17], [26]. The neighborhood information is fused with the original feature in different ways to get a in these studies.…”

Section: A the Proposed Methodsmentioning

confidence: 99%

“…In [17], original picture is fused with the picture which incorporate local information and region-level information of original picture. In [16], original picture is fused with bilateral filtered picture. In this paper, we proposed an adaptive way to fuse original picture and filtered picture.…”

Section: B Fcm With Neighborhood Informationmentioning

confidence: 99%

“…In FRFCM [14] and LRFCM [15], morphological reconstruction is used to processing the noise in advance. In FCM_SICM [16], bilateral filter is used in advance. In LRFCM [15], tight wavelet transform is used to extract features.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Improved Superpixel-based Fuzzy C-Means Method for Complex Picture Segmentation Tasks

Chen

Che

Leung

et al. 2022

2022 14th International Conference on Advanced Computational Intelligence (ICACI)

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Fuzzy c-means(FCM) has attracted wide attentions on picture segmentation as its fuzzy attribute matches the histogram distribution of a picture. However, the fuzzy c-means for the segmentation of a picture with massy noises is barely investigated. In this paper, an improved superpixel-based fuzzy c-means is proposed to segment a massy noise corrupted picture into more than two classes. Firstly, bilateral filtering is used to reduce the compact of noises and makes the picture smoother. Secondly an adaptive method is proposed to fuse the features of the original picture with filtered features. Thirdly simple linearly iterative clustering(SLIC) is used to detect the edge of the picture to avoid over-segmentation. Finally, the histogram-based fuzzy cmeans is used to get the segmentation result. In the experiments, the results show the proposed method achieves a 0.004 ∼ 0.014 higher mPA and 0.004 ∼ 0.06 higher mIoU than other seven algorithms. Besides the segmentation results also show that the over-segmentation is reduced.

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Section: A the Proposed Methodsmentioning

confidence: 99%

Section: B Fcm With Neighborhood Informationmentioning

confidence: 99%

See 1 more Smart Citation

An Improved Superpixel-based Fuzzy C-Means Method for Complex Picture Segmentation Tasks

Chen

Che

Leung

et al. 2022

2022 14th International Conference on Advanced Computational Intelligence (ICACI)

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“…Hence by linking the membership function, the number of iterations is reduced to a great extent. is procedure enables minimizing the objective function [8]. e key step in fuzzy clustering is selection of number of clusters and centroid initialization.…”

Section: Literature Surveymentioning

confidence: 99%

Surface Flaw Detection of Plug Valve Material Using Infrared Thermography and Weighted Local Variation Pixel-Based Fuzzy Clustering Technique

Jacintha

Karthikeyan

Sivaprakasam

2022

Advances in Materials Science and Engineering

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This study focuses on the identification and categorization of plug valve defects. We utilize a thermal fluke camera to obtain the plug valve thermal images. The thermal camera utilizes passive infrared thermography towards the identification of plug valve defects such as cracks, porosity, and internal defects. These flaws depict variation in surface temperature induced by heat flux. Infrared thermography is capable of identification of surface flaws such as cracks and subsurface flaws such as porosity. Its flaw identification range is effective only up to a certain depth in the metal. The heat flux variations are clearly visible for surface cracks and subsurface porosity. However, the heat flux shows no fluctuations for internal defects. Hence, to identify the internal defects in the metal, we opt for a combination of passive infrared thermography and dye penetrating test. In the dye penetrating test, a thinned paint is applied over the metal surface that aids in the identification of cracks, porosity, and internal defects as well. The PIT-DPT (passive infrared thermography-dye penetrating test) works in combination with weighted local variation pixel-based fuzzy clustering (WLVPBFC) to measure the depth of the defects. The defects were measured against parametric quantities such as F-value, precision, recall, accuracy, Jaccard index, TP, FP, TN, FN, FP rate, TP rate, and MCC. These parameters depict variations with regard to surface texture and extent of defect level. The PIT-DPT and WLVPBFC techniques identify metal flaws with 87.88% efficiency when evaluated against other existing algorithms.

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“…The lung segmentation for pulmonary emphysema diagnosis is done using FCM algorithm which is proposed based on the fuzzy set theory. FCM [32] is a well-known algorithm for image processing. In the fuzzy set theory, each element in a set holds a degree of belongingness, which is termed as membership degree, and the degree of non-belongingness is associated with elements that do not belong to that set.…”

Section: A Fcm-based Lung Segmentationmentioning

confidence: 99%

Adaptive Local Ternary Pattern on Parameter Optimized-Faster Region Convolutional Neural Network for Pulmonary Emphysema Diagnosis

2021

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Emphysema is a lung disease that occurs due to abnormal alveoli expansion. This chronic disease causes difficulty in breathing which can lead to lung cancer. The progressive destruction of emphysema can be assessed by Computed Tomography (CT) scans and pulmonary function tests. The severity of the disease may extend to a stage where one can risk their life emphasizing the early detection of emphysema. Primary diagnosis can be done using spirometry and CT for early detection of the disease reducing the mortality rates. Difficulties associated with different diagnostic procedures and inter and intraobserver variations have made blooming researches on more computer-aided techniques. This paper intends to develop a computer-aided technique using the improved deep learning strategy. The initial process is image pre-processing, which is performed by histogram equalization and median filtering. Further, the Fuzzy C Means (FCM) clustering is used for segmentation. After segmentation, a new Adaptive Local Ternary Pattern (ALTP) is used for extracting the pattern descriptor, which is further utilized for classification. As a new contribution, the Parameter Optimized-Faster Region Convolutional Neural Network (PO-FRCNN) is developed for performing the diagnosis. The enhancement of pattern formation and deep classification is accomplished by the Improved Red Deer Algorithm (IRDA), which helps to tune the significant parameters that have a positive influence on the accurateness. The benchmark and real-time dataset are used for performing the experimentation. The results show that the proposed method yields the best result and can effectively diagnose emphysema when compared to state-of-the-art techniques.INDEX TERMS Adaptive local ternary pattern, fuzzy C means clustering, modified deep learning, parameter optimized-faster region convolutional neural network, pulmonary emphysema.

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Robust fuzzy c-means clustering algorithm with adaptive spatial & intensity constraint and membership linking for noise image segmentation

Cited by 74 publications

References 35 publications

An Improved Superpixel-based Fuzzy C-Means Method for Complex Picture Segmentation Tasks

An Improved Superpixel-based Fuzzy C-Means Method for Complex Picture Segmentation Tasks

Surface Flaw Detection of Plug Valve Material Using Infrared Thermography and Weighted Local Variation Pixel-Based Fuzzy Clustering Technique

Adaptive Local Ternary Pattern on Parameter Optimized-Faster Region Convolutional Neural Network for Pulmonary Emphysema Diagnosis

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