Caveolae image analysis for pathogen diabetes

This paper proposes a hybrid model to detect the caveolae from the high noise mesangial cell image, which can be used to analyze the pathogen diabetes further. The model combines the automated seeded region growing, self-adaptive canny algorithm, morphological techniques. The experiments show that c...

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Bibliographic Details
Published in2012 5th International Conference on Biomedical Engineering and Informatics pp. 247 - 250
Main Authors Liu, Jinshuo, Wu, Hui, Cao, Qin, Zhang, Baifang, Gu, Yichun, Ren, Mengfei, Li, Han, Verbeek, Fons J.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2012
Subjects
Biomedical imaging
Caveolin Analysis
Data mining
Diabetes
Gray-scale
Image edge detection
Image Mining
Image segmentation
Insulin
Noise
Pathogen Diabetes
Surface morphology
Surface treatment
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ISBN9781467311830
1467311839
DOI10.1109/BMEI.2012.6512955

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Summary:This paper proposes a hybrid model to detect the caveolae from the high noise mesangial cell image, which can be used to analyze the pathogen diabetes further. The model combines the automated seeded region growing, self-adaptive canny algorithm, morphological techniques. The experiments show that caveolae can be segmented out, and be represented as the suitable descriptors used for further data mining steps. Thus, we can determine the relationships between caveolin description and diabetes through image mining.
ISBN:9781467311830
1467311839
DOI:10.1109/BMEI.2012.6512955