Medical image segmentation based on improved fuzzy clustering in robot virtual surgical system

2016 ◽  
Author(s):  
Yidong Bao
Keyword(s):  
Image Segmentation ◽  
Fuzzy Clustering ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Surgical System

scholarly journals Brain Image Segmentation Based on Fuzzy Clustering

2018 ◽  
Vol 28 (3) ◽  
pp. 220
Author(s):  
Shatha J. Mohammed
Keyword(s):  
Image Segmentation ◽  
Performance Evaluation ◽  
Brain Tumour ◽  
Level Set ◽  
Fuzzy Clustering ◽  
Medical Image ◽  
Medical Images ◽  
Medical Image Segmentation ◽  
Brain Image ◽  
The Brain

The segmentation performance is topic to suitable initialization and best configuration of supervisory parameters. In medical image segmentation, the segmentation is very important when the diagnosing becomes very hard in medical images which are not properly illuminated. This paper proposes segmentation of brain tumour image of MRI images based on spatial fuzzy clustering and level set algorithm. After performance evaluation of the proposed algorithm was carried on brain tumour images, the results showed confirm its effectiveness for medical image segmentation, where the brain tumour is detected properly.

Fuzzy Clustering with Multi-Resolution Bilateral Filtering for Medical Image Segmentation

2013 ◽  
Vol 3 (4) ◽  
pp. 47-59 ◽  
Author(s):  
Kai Xiao ◽  
Jianli Li ◽  
Shuangjiu Xiao ◽  
Haibing Guan ◽  
Fang Fang ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Fuzzy Clustering ◽  
Medical Image ◽  
Clustering Algorithms ◽  
Low Frequency ◽  
Image Data ◽  
Bilateral Filter ◽  
Medical Image Segmentation ◽  
Variation Method ◽  
Frequency Noise

Although fuzzy c-means (FCM) algorithm and some of its variants have been extensively widely used in unsupervised medical image segmentation applications in recent years, they more or less suffer from either noise sensitivity or loss of details, which always is a key point to medical image processing. This paper presents a novel FCM variation method that is suitable for medical image segmentation. The proposed method, typically by incorporating multi-resolution bilateral filter which is combined with wavelet thresholding, provides the following advantages: (1) it is less sensitive to both high- and low-frequency noise and removes spurious blobs and noisy spots, (2) it yields more homogeneous clustering regions, and (3) it preserves detail, thus significantly improving clustering performance. By the use of synthetic and multiple-feature magnetic resonance (MR) image data, the experimental results and quantitative analyses suggest that, compared to other fuzzy clustering algorithms, the proposed method further enhances the robustness to noisy images and capacity of detail preservation.

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