scholarly journals A Study on the Application of Fuzzy Information Seeded Region Growing in Brain MRI Tissue Segmentation

2014 ◽  
Vol 2014 ◽  
pp. 1-11
Author(s):  
Chuin-Mu Wang ◽  
Geng-Cheng Lin
Keyword(s):  
Detection Method ◽  
Brain Mri ◽  
Region Growing ◽  
Generation Process ◽  
Tissue Segmentation ◽  
Seeded Region Growing ◽  
Highly Sensitive ◽  
Merging Process ◽  
Edge Detection Method ◽  
Initial Seed

After long-term clinical trials, MRI has been proven to be used in humans harmlessly, and it is popularly used in medical diagnosis. Although MR is highly sensitive, it provides abundant organization information. Therefore, how to transform the multi-spectral images which is easier to be used for doctor’s clinical diagnosis. In this thesis, the fuzzy bidirectional edge detection method is used to solve conventional SRG problem of growing order in the initial seed stages. In order to overcome the problems of the different regions, although it is the same Euclidean distance for region growing and merging process stages, we present the peak detection method to improve them. The standard deviation target generation process (SDTGP) is applied to guarantee the regions merging process does not cause over- or undersegmentation. Experimental results reveal that FISRG segments a multispectral MR image much more effectively than FAST andK-means.

Seeded Region Growing Based on Extension for Multispectral MR Images Classification

2014 ◽  
Vol 1079-1080 ◽  
pp. 872-877
Author(s):  
Yen Che Chang ◽  
Kuei Ting Kuo ◽  
Zih Yi Wang ◽  
Chuin Mu Wang
Keyword(s):  
Cerebral Spinal Fluid ◽  
Brain Mri ◽  
Medical Knowledge ◽  
Region Growing ◽  
Mr Images ◽  
Brain Images ◽  
New Approach ◽  
The Past ◽  
Seeded Region Growing ◽  
Effective Diagnosis

In the past, doctors judged images based on their own medical knowledge. Nowadays, the digital image processing technology can alleviate the burden of judging a large amount of multispectral information and lead to more effective diagnosis of the pathological tissues. In this paper, we propose a new approach of seeded region growing based on extension (SRGBE) to classify tissues from brain MRI. Based on extension, we tried to strengthen the regional definition. First, we use seeded region growing (SRG) to segment brain images. Second, the SRGBE result is further classified by K-means. Finally, we compare the images of gray matter, white matter and cerebral spinal fluid produced by both approaches to demonstrate the performance of SRGBE.

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