Significantly Fast and Robust Fuzzy C-Means Clustering Algorithm Based on Morphological Reconstruction and Membership Filtering

Lei, Tao; Jia, Xiaohong; Zhang, Yanning; He, Lifeng; Meng, Hongying; Nandi, Asoke K.

doi:10.1109/tfuzz.2018.2796074

Cited by 367 publications

(174 citation statements)

References 40 publications

(49 reference statements)

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“…Numerical results reported for synthetic, medical, and color images are provided. In addition, we compare the proposed algorithm with the four classic algorithms in the literature, i.e., 'FCM S1' [7], 'FCM S2' [7], 'FGFCM' [9], and 'FLICM' [10], and five recently proposed algorithms including 'KWFLICM' [11], 'ARKFCM' [12], 'FRFCM' [20], 'WFCM' [16], and 'DSFCM N' [22]. Finally, we conduct ablation studies and analyze the impact of each component in LRFCM.…”

Section: E Reconstruction Of Segmented Imagementioning

confidence: 99%

“…In FGFCM, the spatial scale factor λ s and gray-level scale factor λ g are respectively set to 3 and 5. For FRFCM, according to [20], we select the observed image as the mask image, and generate the marker image with the aid of a square structuring element of size 3 × 3. Moreover, a median filter of size 3 × 3 is applied to the membership filtering.…”

Section: A Parameter Settingsmentioning

confidence: 99%

“…However, their algorithm is time-consuming and its segmentation effects can be further improved. Lei et al [20] report a new algorithm, namely FRFCM, by augmenting morphological reconstruction and membership filtering. It is fast thanks to using gray level histograms.…”

Section: Introductionmentioning

confidence: 99%

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Sparse Regularization-Based Fuzzy C-Means Clustering Incorporating Morphological Grayscale Reconstruction and Wavelet Frames

Wang

Pedrycz

Zhou

et al. 2021

IEEE Trans. Fuzzy Syst.

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Instead of directly utilizing an observed image including some outliers, noise or intensity inhomogeneity, the use of its ideal value (e.g. noise-free image) has a favorable impact on clustering. Hence, the accurate estimation of the residual (e.g. unknown noise) between the observed image and its ideal value is an important task. To do so, we propose an ℓ0 regularization-based Fuzzy C-Means (FCM) algorithm incorporating a morphological reconstruction operation and a tight wavelet frame transform. To achieve a sound trade-off between detail preservation and noise suppression, morphological reconstruction is used to filter an observed image. By combining the observed and filtered images, a weighted sum image is generated. Since a tight wavelet frame system has sparse representations of an image, it is employed to decompose the weighted sum image, thus forming its corresponding feature set. Taking it as data for clustering, we present an improved FCM algorithm by imposing an ℓ0 regularization term on the residual between the feature set and its ideal value, which implies that the favorable estimation of the residual is obtained and the ideal value participates in clustering. Spatial information is also introduced into clustering since it is naturally encountered in image segmentation. Furthermore, it makes the estimation of the residual more reliable. To further enhance the segmentation effects of the improved FCM algorithm, we also employ the morphological reconstruction to smoothen the labels generated by clustering. Finally, based on the prototypes and smoothed labels, the segmented image is reconstructed by using a tight wavelet frame reconstruction operation. Experimental results reported for synthetic, medical, and color images show that the proposed algorithm is effective and efficient, and outperforms other algorithms.

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Section: E Reconstruction Of Segmented Imagementioning

confidence: 99%

Section: A Parameter Settingsmentioning

confidence: 99%

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Sparse Regularization-Based Fuzzy C-Means Clustering Incorporating Morphological Grayscale Reconstruction and Wavelet Frames

Wang

Pedrycz

Zhou

et al. 2021

IEEE Trans. Fuzzy Syst.

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show abstract

“…where

u_{ik}^{*}

refers to the modified membership function proposed through this article and w i refers to the SOM prototype, and ‘ α ’ relates to the nearest neighbor to be controlled. In certain advancements of clustering process motivated by Lei et al, complete spatial information of input image is employed for the estimation of the modified membership fuzziness function

u_{ik}^{*}

and it is denoted in Equation . We use Equation for the implementation of modified membership function in the fuzzy clustering approach that endeavors to decrease the computational complexity.

u_{ik}^{*} = u_{ik} + \sum_{\begin{matrix} r \in N_{r} \\ r \neq i \end{matrix}} G_{ir} u_{ir}

…”

Section: The Contribution Of Our Articlementioning

confidence: 99%

Unsupervised learning‐based clustering approach for smart identification of pathologies and segmentation of tissues in brain magnetic resonance imaging

Vigneshwaran

Govindaraj

Murugan

et al. 2019

Int J Imaging Syst Tech

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Human‐made/developed algorithms provide automatic identification and segmentation of the tissues, lesions and tumor regions available in brain magnetic resonance scan images, which invocates predicaments such as high computational cost and low accuracy rate. Such hassles are reconciled with the utilization of an unsupervised approach in combination with clustering techniques. Initially, static features are chosen from the input image, which is fed to the self‐organizing map (SOM), where the algorithm employs the dimensionality reduction of input images. Consecutively, the reduced SOM prototype of data is clustered by the modified fuzzy K‐means (MFKM) algorithm. The MFKM algorithm can be modified in terms of membership variables because it operates with spatial information and converges quickly, and this would be of greater benefit to radiologists as they reduce the wrong predictions and voluminous time that normally occur owing to human involvement. The proposed algorithm provides 98.77% sensitivity and 97.5% specificity, which are better than any other traditional algorithms mentioned in this article.

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“…Therefore, the question arises how one can maintain local spatial information while reducing the computational complexity efficiently. Lei et al [32] proposed a fast and robust FCM algorithm (FRFCM) to address the problem by employing morphological reconstruction [33] and membership filtering. Because the repeated distance computation between pixels within neighborhood window and clustering centers is removed, the algorithm is very fast and provides a better segmentation result than state-of-the-art algorithms.…”

Section: Introductionmentioning

confidence: 99%

Superpixel-Based Fast Fuzzy C-Means Clustering for Color Image Segmentation

Lei

Jia

Zhang

et al. 2019

IEEE Trans. Fuzzy Syst.

Self Cite

244

146

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A great number of improved fuzzy c-means (FCM) clustering algorithms have been widely used for grayscale and color image segmentation. However, most of them are timeconsuming and unable to provide desired segmentation results for color images due to two reasons. The first one is that the incorporation of local spatial information often causes a high computational complexity due to the repeated distance computation between clustering centers and pixels within a local neighboring window. The other one is that a regular neighboring window usually breaks up the real local spatial structure of images and thus leads to a poor segmentation. In this work, we propose a superpixel-based fast FCM clustering algorithm (SFFCM) that is significantly faster and more robust than stateof-the-art clustering algorithms for color image segmentation. To obtain better local spatial neighborhoods, we firstly define a multiscale morphological gradient reconstruction (MMGR) operation to obtain a superpixel image with accurate contour. In contrast to traditional neighboring window of fixed size and shape, the superpixel image provides better adaptive and irregular local spatial neighborhoods that are helpful for improving color image segmentation. Secondly, based on the obtained superpixel image, the original color image is simplified efficiently and its histogram is computed easily by counting the number of pixels in each region of the superpixel image. Finally, we implement FCM with histogram parameter on the superpixel image to obtain the final segmentation result. Experiments performed on synthetic images and real images demonstrate that the proposed algorithm provides better segmentation results and takes less time than state-of-the-art clustering algorithms for color image segmentation.

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Significantly Fast and Robust Fuzzy C-Means Clustering Algorithm Based on Morphological Reconstruction and Membership Filtering

Cited by 367 publications

References 40 publications

Sparse Regularization-Based Fuzzy C-Means Clustering Incorporating Morphological Grayscale Reconstruction and Wavelet Frames

Sparse Regularization-Based Fuzzy C-Means Clustering Incorporating Morphological Grayscale Reconstruction and Wavelet Frames

Unsupervised learning‐based clustering approach for smart identification of pathologies and segmentation of tissues in brain magnetic resonance imaging

Superpixel-Based Fast Fuzzy C-Means Clustering for Color Image Segmentation

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