scholarly journals DNN-Based Brain MRI Classification Using Fuzzy Clustering and Autoencoder Features

2021 ◽  
Vol 21 (4) ◽  
pp. 349-357
Author(s):  
Nishant Chauhan ◽  
Byung-Jae Choi
Keyword(s):  
Fuzzy Clustering ◽  
Brain Mri

scholarly journals Semi-Supervised Rough Fuzzy Clustering for Brain MRI Segmentation

2019 ◽  
Vol 7 (3) ◽  
pp. 1287-1294
Author(s):  
Nur Alom Talukdar ◽  
Anindya Halder
Keyword(s):  
Fuzzy Clustering ◽  
Brain Mri ◽  
Mri Segmentation

Automatic Segmentation of Thalamus From Brain MRI Integrating Fuzzy Clustering and Dynamic Contours

2004 ◽  
Vol 51 (5) ◽  
pp. 800-811 ◽  
Author(s):  
L. Amini ◽  
H. Soltanian-Zadeh ◽  
C. Lucas ◽  
M. Gity
Keyword(s):  
Fuzzy Clustering ◽  
Automatic Segmentation ◽  
Brain Mri ◽  
Dynamic Contours

Optimized Fuzzy Clustering Algorithms for Brain MRI Image Segmentation Based on Local Gaussian Probability and Anisotropic Weight Models

2018 ◽  
Vol 32 (09) ◽  
pp. 1857005 ◽  
Author(s):  
Ting Zhang
Keyword(s):  
Image Segmentation ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Clinical Medicine ◽  
Brain Mri ◽  
Brain Magnetic Resonance Imaging ◽  
Segmentation Algorithm ◽  
Fuzzy C Means ◽  
Anisotropic Weight ◽  
Mri Image

Brain Magnetic Resonance Imaging (MRI) image segmentation is one of the critical technologies of clinical medicine, and is the basis of three-dimensional reconstruction and downstream analysis between normal tissues and diseased tissues. However, there are various limitations in brain MRI images, such as gray irregularities, noise, and low contrast, reducing the accuracy of the brain MRI images segmentation. In this paper, we propose two optimization solutions for the fuzzy clustering algorithm based on local Gaussian probability fuzzy C-means (LGP-FCM) model and anisotropic weight fuzzy C-means (AW-FCM) model and apply it in brain MRI image segmentation. An FCM clustering algorithm is proposed based on AW-FCM. By introducing the new neighborhood weight calculation method, each point has the weight of anisotropy, effectively overcomes the influence of noise on the image segmentation. In addition, the LGP model is introduced in the objective function of fuzzy clustering, and a fuzzy clustering segmentation algorithm based on LGP-FCM is proposed. A clustering segmentation algorithm of adaptive scale fuzzy LGP model is proposed. The neighborhood scale corresponding to each pixel in the image is automatically estimated, which improves the robustness of the model and achieves the purpose of precise segmentation. Extensive experimental results demonstrate that the proposed LGP-FCM algorithm outperforms comparison algorithms in terms of sensitivity, specificity and accuracy. LGP-FCM can effectively segment the target regions from brain MRI images.

scholarly journals A New Multistage Medical Segmentation Method Based on Superpixel and Fuzzy Clustering

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Shiyong Ji ◽  
Benzheng Wei ◽  
Zhen Yu ◽  
Gongping Yang ◽  
Yilong Yin
Keyword(s):  
Fuzzy Clustering ◽  
Brain Mri ◽  
Medical Image Segmentation ◽  
Segmentation Method ◽  
Imaging Characteristic ◽  
Multi Stage ◽  
Segmentation Accuracy ◽  
Medical Segmentation ◽  
Influence Of Noise ◽  
Mri Image

The medical image segmentation is the key approach of image processing for brain MRI images. However, due to the visual complex appearance of image structures and the imaging characteristic, it is still challenging to automatically segment brain MRI image. A new multi-stage segmentation method based on superpixel and fuzzy clustering (MSFCM) is proposed to achieve the good brain MRI segmentation results. The MSFCM utilizes the superpixels as the clustering objects instead of pixels, and it can increase the clustering granularity and overcome the influence of noise and bias effectively. In the first stage, the MRI image is parsed into several atomic areas, namely, superpixels, and a further parsing step is adopted for the areas with bigger gray variance over setting threshold. Subsequently, designed fuzzy clustering is carried out to the fuzzy membership of each superpixel, and an iterative broadcast method based on the Butterworth function is used to redefine their classifications. Finally, the segmented image is achieved by merging the superpixels which have the same classification label. The simulated brain database from BrainWeb site is used in the experiments, and the experimental results demonstrate that MSFCM method outperforms the traditional FCM algorithm in terms of segmentation accuracy and stability for MRI image.

Brain MRI Tissue Classification Based Fuzzy Clustering with Competitive Learning

2017 ◽  
Vol 7 (7) ◽  
pp. 1654-1659
Author(s):  
Yuanpeng Zhang ◽  
Fei Tian ◽  
Huiqun Wu ◽  
Xingyun Geng ◽  
Danmin Qian ◽  
...  
Keyword(s):  
Fuzzy Clustering ◽  
Brain Mri ◽  
Competitive Learning ◽  
Tissue Classification

scholarly journals Automatic segmentation of meningioma from non-contrasted brain MRI integrating fuzzy clustering and region growing

2011 ◽  
Vol 11 (1) ◽  
Author(s):  
Thomas M Hsieh ◽  
Yi-Min Liu ◽  
Chun-Chih Liao ◽  
Furen Xiao ◽  
I-Jen Chiang ◽  
...  
Keyword(s):  
Fuzzy Clustering ◽  
Automatic Segmentation ◽  
Brain Mri ◽  
Region Growing

Multi-View Fuzzy Clustering and Its Application for Brain MRI Segmentation

2019 ◽  
Vol 9 (7) ◽  
pp. 1541-1546
Author(s):  
Guanglei Sheng ◽  
Xiujian Hu ◽  
Chao Zhang ◽  
Shuang Jia
Keyword(s):  
Fuzzy Clustering ◽  
Brain Mri ◽  
Mri Segmentation
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