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Tumor detection from small animal PET using clustering based on intensity and connectivity

Title
Tumor detection from small animal PET using clustering based on intensity and connectivity
Authors
Lee J.M.Song S.M.Kim K.M.Kim M.-H.
Ewha Authors
김명희
SCOPUS Author ID
김명희scopus
Issue Date
2007
Journal Title
IFMBE Proceedings
ISSN
1680-0737JCR Link
Citation
vol. 14, no. 1, pp. 2580 - 2583
Keywords
Fuzzy c-means clusteringGeometric clusteringPET of the tumor bearing small animalQuantitative measurementTumor detection
Publisher
Springer Verlag
Indexed
SCOPUS scopus
Abstract
We present an efficient clustering method for detecting the tumor in positron emission tomography(PET) of the tumor bearing small animal. We used iterative threshold method to remove the background noise and then we applied two clustering procedures in order. The one is clustering method based on intensity to segment the tumor region and the other is clustering based on connectivity to remove false positive region from the segmented region. The tumor tissue looks bright in the image compared to surrounding normal tissue because of glucose uptake. Therefore, based on volume intensity, we divided all elements of the image into several clusters, the tumor, living bodies, background using improved fuzzy cmeans clustering(FCM). Using FCM with the sorted initial mean of each cluster gets out of the wrong optimization and reduces the amount of time-consumed. However, not only the tumor tissue, but also the other organs like heart, bladder can also have high intensity value because of glucose metabolism. So in order to separate the tumor and false positive region, we applied geometric clustering based on connectivity. Proposed segmentation method can lead a robust analysis of the tumor growth with the aid of the quantitative measurements such the tumor size or volume. © International Federation for Medical and Biological Engineering 2007.
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엘텍공과대학 > 컴퓨터공학과 > Journal papers
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