On comparison of manifold learning techniques for dendritic spine classification | IEEE Conference Publication | IEEE Xplore

On comparison of manifold learning techniques for dendritic spine classification


Abstract:

Dendritic spines are one of the key functional components of neurons. Their morphological changes are correlated with neuronal activity. Neuroscientists study spine shape...Show More

Abstract:

Dendritic spines are one of the key functional components of neurons. Their morphological changes are correlated with neuronal activity. Neuroscientists study spine shape variations to understand their relation with neuronal activity. Currently this analysis performed manually, the availability of reliable automated tools would assist neuroscientists and accelerate this research. Previously, morphological features based spine analysis has been performed and reported in the literature. In this paper, we explore the idea of using and comparing manifold learning techniques for classifying spine shapes. We start with automatically segmented data and construct our feature vector by stacking and concatenating the columns of images. Further, we apply unsupervised manifold learning algorithms and compare their performance in the context of dendritic spine classification. We achieved 85.95% accuracy on a dataset of 242 automatically segmented mushroom and stubby spines. We also observed that ISOMAP implicitly computes prominent features suitable for classification purposes.
Date of Conference: 13-16 April 2016
Date Added to IEEE Xplore: 16 June 2016
Electronic ISBN:978-1-4799-2349-6
Electronic ISSN: 1945-8452
Conference Location: Prague, Czech Republic

References

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