Rough Kernelized Fuzzy C-Means Based Medical Image Segmentation

2017 ◽  
pp. 466-474 ◽  
Author(s):  
Amiya Halder ◽  
Siddhartha Guha
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Fuzzy C Means

scholarly journals D-FCM: Density based fuzzy c-means clustering algorithm with application in medical image segmentation

2017 ◽  
Vol 122 ◽  
pp. 407-414 ◽  
Author(s):  
Hua-Xin Pei ◽  
Zeng-Rong Zheng ◽  
Chen Wang ◽  
Chun-Na Li ◽  
Yuan-Hai Shao
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Clustering Algorithm ◽  
Medical Image Segmentation ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering

scholarly journals Digital Medical Image Segmentation Using Fuzzy C-Means Clustering

2020 ◽  
Vol 4 (1) ◽  
pp. 51 ◽  
Author(s):  
Bakhtyar Ahmed Mohammed ◽  
Muzhir Shaban Al-Ani
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Brain Mri ◽  
Accurate Method ◽  
Medical Image Segmentation ◽  
Fuzzy C Means ◽  
Fcm Clustering ◽  
The Brain ◽  
Digital Medical Image

In the modern globe, digital medical image processing is a major branch to study in the fields of medical and information technology. Every medical field relies on digital medical imaging in diagnosis for most of their cases. One of the major components of medical image analysis is medical image segmentation. Medical image segmentation participates in the diagnosis process, and it aids the processes of other medical image components to increase the accuracy. In unsupervised methods, fuzzy c-means (FCM) clustering is the most accurate method for image segmentation, and it can be smooth and bear desirable outcomes. The intention of this study is to establish a strong systematic way to segment complicate medical image cases depend on the proposed method to share in the decision-making process. This study mentions medical image modalities and illustrates the steps of the FCM clustering method mathematically with example. It segments magnetic resonance imaging (MRI) of the brain to separate tumor inside the brain MRI according to four statuses.

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