Title :
Automatic Segmenting and Classifying the Neural Stem Cells in Adherent Culturing Condition
Author :
Xiang Qian ; Datian Ye
Author_Institution :
Dept. of Biomed. Eng., Tsinghua Univ., Beijing, China
Abstract :
The neural stem cells (NSCs) have a wide range of perspectives in clinical applications for neurology disorders due to their multi-potent potentials of differentiation. Automatic segment and classify the NSCs can be useful tools for biologist to monitor the progress of differentiation. In this paper, a hybrid image segmentation framework based on self-organizing map and watershed algorithm was applied to segment the NSCs in adherent culturing conditions. The cells shapes were analyzed using Fourier descriptors and classified using a feed-forward neural network. The results indicated that different shapes of NSCs in adherent culturing condition can be successfully segmented and classified based on these methods.
Keywords :
biomedical imaging; cellular biophysics; feedforward; feedforward neural nets; image classification; image segmentation; medical image processing; neurophysiology; Fourier descriptors; adherent culturing condition; automatic classification; automatic segmentation; cell shapes; feed-forward neural network; hybrid image segmentation; neural stem cells; self-organizing map; watershed algorithm; Biomedical engineering; Biomedical monitoring; Clustering algorithms; Image analysis; Image segmentation; Nervous system; Neurons; Principal component analysis; Shape; Stem cells;
Conference_Titel :
Biomedical Engineering and Informatics, 2009. BMEI '09. 2nd International Conference on
Conference_Location :
Tianjin
Print_ISBN :
978-1-4244-4132-7
Electronic_ISBN :
978-1-4244-4134-1
DOI :
10.1109/BMEI.2009.5304916
