Ghani, Muhammad UsmanMesadi, FitsumKanik, Sumeyra DemirArgunsah, Ali OzgurIsraely, InbalUnay, DevrimTasdizen, Tolga2023-06-162023-06-162016978-1-4799-2349-6978-1-4799-2350-21945-7928https://doi.org/10.1109/ISBI.2016.7493280https://hdl.handle.net/20.500.14365/195913th IEEE International Symposium on Biomedical Imaging (ISBI) -- APR 13-16, 2016 -- Prague, CZECH REPUBLICAnalysis of dendritic spines is an essential task to understand the functional behavior of neurons. Their shape variations are known to be closely linked with neuronal activities. Spine shape analysis in particular, can assist neuroscientists to identify this relationship. A novel shape representation has been proposed recently, called Disjunctive Normal Shape Models (DNSM). DNSM is a parametric shape representation and has proven to be successful in several segmentation problems. In this paper, we apply this parametric shape representation as a feature extraction algorithm. Further, we propose a kernel density estimation (KDE) based classification approach for dendritic spine classification. We evaluate our proposed approach on a data set of 242 spines, and observe that it outperforms the classical morphological feature based approach for spine classification. Our probabilistic framework also provides a way to examine the separability of spine shape classes in the likelihood ratio space, which leads to further insights about the nature of the shape analysis problem in this context.eninfo:eu-repo/semantics/closedAccessDisjunctive Normal Shape ModelSpine ClassificationShape analysisKernel density estimationmicroscopyneuroimagingConference Object10.1109/ISBI.2016.74932802-s2.0-84978376873