  Functional properties of neurons are strongly coupled with their morphology.
Changes in neuronal activity alter morphological characteristics of dendritic
spines. First step towards understanding the structure-function relationship is
to group spines into main spine classes reported in the literature. Shape
analysis of dendritic spines can help neuroscientists understand the underlying
relationships. Due to unavailability of reliable automated tools, this analysis
is currently performed manually which is a time-intensive and subjective task.
Several studies on spine shape classification have been reported in the
literature, however, there is an on-going debate on whether distinct spine
shape classes exist or whether spines should be modeled through a continuum of
shape variations. Another challenge is the subjectivity and bias that is
introduced due to the supervised nature of classification approaches. In this
paper, we aim to address these issues by presenting a clustering perspective.
In this context, clustering may serve both confirmation of known patterns and
discovery of new ones. We perform cluster analysis on two-photon microscopic
images of spines using morphological, shape, and appearance based features and
gain insights into the spine shape analysis problem. We use histogram of
oriented gradients (HOG), disjunctive normal shape models (DNSM), morphological
features, and intensity profile based features for cluster analysis. We use
x-means to perform cluster analysis that selects the number of clusters
automatically using the Bayesian information criterion (BIC). For all features,
this analysis produces 4 clusters and we observe the formation of at least one
cluster consisting of spines which are difficult to be assigned to a known
class. This observation supports the argument of intermediate shape types.
