A Novel Type-2 Fuzzy <i>C</i>-Means Clustering for Brain MR Image Segmentation

Mishro, Pranaba K.; Agrawal, Sanjay; Panda, Rutuparna; Abraham, Ajith

doi:10.1109/tcyb.2020.2994235

Cited by 70 publications

(45 citation statements)

References 34 publications

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“…Zhang et al [ 33 ] proposed a novel hidden Markov random field (HMRF) model which can encode spatial information through the mutual influences of neighboring sites to improve its accuracy and robustness. K. Mishro et al [ 34 ] proposed a type-2 AWSFCM clustering algorithm to perform segmentation tasks. It assigned the problematic equidistant pixels to a single cluster by offering larger weight to pixel closing to the expected decision boundary.…”

Section: Related Workmentioning

confidence: 99%

Learning U-Net Based Multi-Scale Features in Encoding-Decoding for MR Image Brain Tissue Segmentation

Long

Song

et al. 2021

Sensors

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Accurate brain tissue segmentation of MRI is vital to diagnosis aiding, treatment planning, and neurologic condition monitoring. As an excellent convolutional neural network (CNN), U-Net is widely used in MR image segmentation as it usually generates high-precision features. However, the performance of U-Net is considerably restricted due to the variable shapes of the segmented targets in MRI and the information loss of down-sampling and up-sampling operations. Therefore, we propose a novel network by introducing spatial and channel dimensions-based multi-scale feature information extractors into its encoding-decoding framework, which is helpful in extracting rich multi-scale features while highlighting the details of higher-level features in the encoding part, and recovering the corresponding localization to a higher resolution layer in the decoding part. Concretely, we propose two information extractors, multi-branch pooling, called MP, in the encoding part, and multi-branch dense prediction, called MDP, in the decoding part, to extract multi-scale features. Additionally, we designed a new multi-branch output structure with MDP in the decoding part to form more accurate edge-preserving predicting maps by integrating the dense adjacent prediction features at different scales. Finally, the proposed method is tested on datasets MRbrainS13, IBSR18, and ISeg2017. We find that the proposed network performs higher accuracy in segmenting MRI brain tissues and it is better than the leading method of 2018 at the segmentation of GM and CSF. Therefore, it can be a useful tool for diagnostic applications, such as brain MRI segmentation and diagnosing.

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Section: Related Workmentioning

confidence: 99%

Learning U-Net Based Multi-Scale Features in Encoding-Decoding for MR Image Brain Tissue Segmentation

Long

Song

et al. 2021

Sensors

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“…Different from the previous five algorithms, as both NDFCM and FRFCM only compute the local spatial information of images once in the process of image segmentation, they have low computational complexity. Because the SSFCA involves two computational steps, [33] O(n(w + 1) 2 + nw 2 ct) NDFCM [46] O(nw 2 + lct) FRFCM [47] O(nw 2 + lct) DSFCM_N [13] O(nw 2 ct) MSFCM [20] O(nct) AWSFCM [34] O(K (nw 2 + 2nc)t) RSSFCA O(nct + n(M (c) + c)t)…”

Section: Computational Complexitymentioning

confidence: 99%

“…Early improved FCM algorithms usually employ a fixed-size window of size w × w to obtain local spatial information such as FCM_S [12], FCM_S1 [29], FCM_S2 [29], FLICM [30], KWFLICM [33], DSFCM_N [13] etc. Similar to the above algorithms, recently, Mishro et al [34] proposed a novel type-2 adaptive weighted spatial FCM (AWSFCM) clustering algorithm that employs a fuzzy linguistic fuzzifier and spatial information of membership to reduce misclassification of pixels. However, in practical applications, a large window usually leads to rich spatial information but high computational cost; on the contrary, a small window leads to low computational cost but limited spatial information.…”

Section: Introductionmentioning

confidence: 99%

Robust Self-Sparse Fuzzy Clustering for Image Segmentation

Jia

Lei

et al. 2020

IEEE Access

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Traditional fuzzy clustering algorithms suffer from two problems in image segmentations. One is that these algorithms are sensitive to outliers due to the non-sparsity of fuzzy memberships. The other is that these algorithms often cause image over-segmentation due to the loss of image local spatial information. To address these issues, we propose a robust self-sparse fuzzy clustering algorithm (RSSFCA) for image segmentation. The proposed RSSFCA makes two contributions. The first concerns a regularization under Gaussian metric that is integrated into the objective function of fuzzy clustering algorithms to obtain fuzzy membership with sparsity, which reduces a proportion of noisy features and improves clustering results. The second concerns a connected-component filtering based on area density balance strategy (CCF-ADB) that is proposed to address the problem of image over-segmentation. Compared to the integration of local spatial information into the objective functions, the presented CCF-ADB is simpler and faster for the removal of small areas. Experimental results show that the proposed RSSFCA addresses two problems in current fuzzy clustering algorithms, i.e., the outlier sensitivity and the over-segmentation, and it provides better image segmentation results than state-of-the-art algorithms.

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“…Other emotional state indicators include gait [69], facial cues [70], or a combination thereof [71]. Alternatively, human emotional state can be estimated by brain imaging techniques [72].…”

Section: Previous Workmentioning

confidence: 99%

Extending Fuzzy Cognitive Maps with Tensor-Based Distance Metrics

et al. 2020

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Cognitive maps are high level representations of the key topological attributes of real or abstract spatial environments progressively built by a sequence of noisy observations. Currently such maps play a crucial role in cognitive sciences as it is believed this is how clusters of dedicated neurons at hippocampus construct internal representations. The latter include physical space and, perhaps more interestingly, abstract fields comprising of interconnected notions such as natural languages. In deep learning cognitive graphs are effective tools for simultaneous dimensionality reduction and visualization with applications among others to edge prediction, ontology alignment, and transfer learning. Fuzzy cognitive graphs have been proposed for representing maps with incomplete knowledge or errors caused by noisy or insufficient observations. The primary contribution of this article is the construction of cognitive map for the sixteen Myers-Briggs personality types with a tensor distance metric. The latter combines two categories of natural language attributes extracted from the namesake Kaggle dataset. To the best of our knowledge linguistic attributes are separated in categories. Moreover, a fuzzy variant of this map is also proposed where a certain personality may be assigned to up to two types with equal probability. The two maps were evaluated based on their topological properties, on their clustering quality, and on how well they fared against the dataset ground truth. The results indicate a superior performance of both maps with the fuzzy variant being better. Based on the findings recommendations are given for engineers and practitioners.

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A Novel Type-2 Fuzzy C-Means Clustering for Brain MR Image Segmentation

Cited by 70 publications

References 34 publications

Learning U-Net Based Multi-Scale Features in Encoding-Decoding for MR Image Brain Tissue Segmentation

Learning U-Net Based Multi-Scale Features in Encoding-Decoding for MR Image Brain Tissue Segmentation

Robust Self-Sparse Fuzzy Clustering for Image Segmentation

Extending Fuzzy Cognitive Maps with Tensor-Based Distance Metrics

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