An Adaptive Feature Selection Algorithm for Fuzzy Clustering Image Segmentation Based on Embedded Neighbourhood Information Constraints

Ren, Hang; Hu, Taotao

doi:10.3390/s20133722

Cited by 5 publications

(4 citation statements)

References 53 publications

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“…A common method based on region, the image starts from a certain point and merges the surrounding pixel points with the same attribute (including gray value, texture and other features) (47). There are also some segmentation algorithms based on specific theories, such as minimized graph cut algorithm based on energy (48), conditional random field method based on statistics (49), and clustering analysis method based on fuzzy sets (50), etc. In addition, the deep learning network can automatically obtain features from the training data and achieve good segmentation performance (51,52).…”

Section: Machine Learning Modementioning

confidence: 99%

Application of Artificial Intelligence in Diagnosis of Craniopharyngioma

Qin

Wang

et al. 2022

Front. Neurol.

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Craniopharyngioma is a congenital brain tumor with clinical characteristics of hypothalamic-pituitary dysfunction, increased intracranial pressure, and visual field disorder, among other injuries. Its clinical diagnosis mainly depends on radiological examinations (such as Computed Tomography, Magnetic Resonance Imaging). However, assessing numerous radiological images manually is a challenging task, and the experience of doctors has a great influence on the diagnosis result. The development of artificial intelligence has brought about a great transformation in the clinical diagnosis of craniopharyngioma. This study reviewed the application of artificial intelligence technology in the clinical diagnosis of craniopharyngioma from the aspects of differential classification, prediction of tissue invasion and gene mutation, prognosis prediction, and so on. Based on the reviews, the technical route of intelligent diagnosis based on the traditional machine learning model and deep learning model were further proposed. Additionally, in terms of the limitations and possibilities of the development of artificial intelligence in craniopharyngioma diagnosis, this study discussed the attentions required in future research, including few-shot learning, imbalanced data set, semi-supervised models, and multi-omics fusion.

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Section: Machine Learning Modementioning

confidence: 99%

Application of Artificial Intelligence in Diagnosis of Craniopharyngioma

Qin

Wang

et al. 2022

Front. Neurol.

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“…Step 3. If the relation 􏽐 n j�1 n j < n exists, return to Step 2, and vice versa build the hierarchical generalized function network [21,22].…”

Section: Two-input Single-output Generalized Network Modelmentioning

confidence: 99%

Personalized Recommendation Algorithm for Interactive Medical Image Using Deep Learning

Liu

Guo

2022

Mathematical Problems in Engineering

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Personalized interactive image recommendation has several issues, such as being slow or having poor recommendation quality. Therefore, we propose an image personalized recommendation algorithm (IPRA) using deep learning to improve the time and quality of personalized interactive image recommendations. First, the feature subimage is obtained and converted into a one-dimensional vector using the convolution neural network model. Single input and single output functional and dual input and single output generalized functional network model are integrated into the model to improve the learning ability of nonlinear mapping and avoid overfitting during the training process; second, a one-dimensional vector is clustered using the fuzzy k-means approach and then translated into hyperbolic space; Finally, the Poincare map model is used to map the updated vector, the transformed vector is mapped using the PM model, and the image information is fed back to the two-dimensional plane, and the image recommendation set is formed based on the ranking of similarity, and the visual recommendation is presented to the user. The results show that the size of the convolution kernel is 2 × 2, and the image one-dimensional vector clustering can be better completed. The optimal value of F1 is 0.92, and the optimal value of average time is 11 s. The image recommendation quality is better, and the image recommendation can be formed according to the photographic similarity, which has good application value.

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“…Nowadays, a variety of image segmentation techniques have been proposed, such as algorithms based on manual segmentation [2,3], boundary [4], atlas [5][6][7], kernel function [8][9][10][11], region growing technology [12][13][14][15][16] and clustering [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32]. For the effectiveness and accuracy, the clustering-based segmentation has become the most popular method to classify different property elements.…”

Section: Introductionmentioning

confidence: 99%

“…Many experiments [28][29][30][31][32] show that combining the neighborhood information is beneficial to improving the robustness. To obtain a robust clustering, Bai et al merged the spatial information into the objective function [28], but it suffered defeat when classifying normal brain tissue.…”

Section: Introductionmentioning

confidence: 99%

Intuitionistic Fuzzy C-Means Algorithm Based on Membership Information Transfer-Ring and Similarity Measurement

Chen

Xie

Huang

et al. 2021

Sensors

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The fuzzy C-means clustering (FCM) algorithm is used widely in medical image segmentation and suitable for segmenting brain tumors. Therefore, an intuitionistic fuzzy C-means algorithm based on membership information transferring and similarity measurements (IFCM-MS) is proposed to segment brain tumor magnetic resonance images (MRI) in this paper. The original FCM lacks spatial information, which leads to a high noise sensitivity. To address this issue, the membership information transfer model is adopted to the IFCM-MS. Specifically, neighborhood information and the similarity of adjacent iterations are incorporated into the clustering process. Besides, FCM uses simple distance measurements to calculate the membership degree, which causes an unsatisfactory result. So, a similarity measurement method is designed in the IFCM-MS to improve the membership calculation, in which gray information and distance information are fused adaptively. In addition, the complex structure of the brain results in MRIs with uncertainty boundary tissues. To overcome this problem, an intuitive fuzzy attribute is embedded into the IFCM-MS. Experiments performed on real brain tumor images demonstrate that our IFCM-MS has low noise sensitivity and high segmentation accuracy.

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An Adaptive Feature Selection Algorithm for Fuzzy Clustering Image Segmentation Based on Embedded Neighbourhood Information Constraints

Cited by 5 publications

References 53 publications

Application of Artificial Intelligence in Diagnosis of Craniopharyngioma

Application of Artificial Intelligence in Diagnosis of Craniopharyngioma

Personalized Recommendation Algorithm for Interactive Medical Image Using Deep Learning

Intuitionistic Fuzzy C-Means Algorithm Based on Membership Information Transfer-Ring and Similarity Measurement

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