Segmentation and Classification of Brain Anatomy in Magnetic Resonance Images Based on Fuzzy C-Means Clustering Algorithm

2017 ◽  
Vol 7 (4) ◽  
pp. 844-849 ◽  
Author(s):  
Dan Chen ◽  
Chuanbing Jin ◽  
Yongsheng Cao ◽  
Guangming Hu
Keyword(s):  
Magnetic Resonance ◽  
Clustering Algorithm ◽  
Magnetic Resonance Images ◽  
Brain Anatomy ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering

Artificial Intelligence based myelinated white Matter Segmentation for a Pediatricbrain –a Challenging Task

2020 ◽  
Vol 13 ◽  
Author(s):  
S. Jacily Jemila ◽  
A. Brintha Therese
Keyword(s):  
Artificial Intelligence ◽  
White Matter ◽  
Magnetic Resonance ◽  
Signal To Noise Ratio ◽  
Medical Image Analysis ◽  
Magnetic Resonance Images ◽  
Signal To Noise ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Noise Ratio

Background: Segmentation of a baby brain,in particular myelinated white matter is a very challenging and important task in medical image analysis, because of the ongoing process of myelination and structural differences present in magnetic resonance images of a baby. Most available algorithms for segmentation of a baby brain are atlas based segmentations, which may not accurate because baby brain Magnetic Resonance Images (MRI) are very subjective. Objective: Artificial intelligence based methods for myelinated white matter segmentation. Method: Fuzzy C-means Clustering with Level Set Method (FCMLSM), Adaptively Regularized Kernel-based Fuzzy C-means clustering (ARKFCM), Multiplicative Intrinsic Component Optimization (MICO) and Particle Swarm Optimization (PSO). Results: Signal to Noise Ratio (SNR), Edge Preservation Index (EPI), Structural Similarity Index (SSIM) and Peak Signal to noise Ratio (PSNR) Accuracy, Precision, Dice and Jaccard values are maintained good and Mean squared error (MSE) is less for FCMLSM. Conclusion: FCMLSM is a very suitable method for myelinated white matter segmentation when compared to ARKFCM, MICO and PSO.

scholarly journals Automatic measurement of traditional Chinese costume from its silhouette through Fuzzy c-means clustering method

2020 ◽  
Vol 15 ◽  
pp. 155892502097832
Author(s):  
Jiaqin Zhang ◽  
Jingan Wang ◽  
Le Xing ◽  
Hui’e Liang
Keyword(s):  
Industrial Application ◽  
Clustering Algorithm ◽  
Color Space ◽  
Automatic Measurement ◽  
Feature Point ◽  
Feature Points ◽  
Point Location ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Environmental Robustness

As the precious cultural heritage of the Chinese nation, traditional costumes are in urgent need of scientific research and protection. In particular, there are scanty studies on costume silhouettes, due to the reasons of the need for cultural relic protection, and the strong subjectivity of manual measurement, which limit the accuracy of quantitative research. This paper presents an automatic measurement method for traditional Chinese costume dimensions based on fuzzy C-means clustering and silhouette feature point location. The method is consisted of six steps: (1) costume image acquisition; (2) costume image preprocessing; (3) color space transformation; (4) object clustering segmentation; (5) costume silhouette feature point location; and (6) costume measurement. First, the relative total variation model was used to obtain the environmental robustness and costume color adaptability. Second, the FCM clustering algorithm was used to implement image segmentation to extract the outer silhouette of the costume. Finally, automatic measurement of costume silhouette was achieved by locating its feature points. The experimental results demonstrated that the proposed method could effectively segment the outer silhouette of a costume image and locate the feature points of the silhouette. The measurement accuracy could meet the requirements of industrial application, thus providing the dual value of costume culture research and industrial application.

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